Using R in microbial analysis-2
Tao Wen(文涛)
2023-05-08
3 microbial community analysis
microbial community diversity analysis
Code 3A（Example 13）
alpha diverzity
# BiocManager::install("picante")
library(phyloseq)
library(Biostrings)
library(ape)
library(ggtree)
library(phyloseq)
library(Biostrings)
library(vegan)
otu = read.delim("./data/otutab.txt",row.names = 1)
tree = read_tree("./data/otus.tree")
head(otu)otu2 = as.data.frame(t(rrarefy(t(otu),min(colSums(otu)))))
colSums(otu2)
#> KO1 KO2 KO3 KO4 KO5 KO6 OE1 OE2 OE3 OE4 OE5 OE6 WT1
#> 31609 31609 31609 31609 31609 31609 31609 31609 31609 31609 31609 31609 31609
#> WT2 WT3 WT4 WT5 WT6
#> 31609 31609 31609 31609 31609
otul = decostand(t(otu),'log') %>% t()
otu.alp = t(otu2)
# Richness
observed_species <- estimateR(otu.alp)[1, ]
# Chao 1
Chao1 <- estimateR(otu.alp)[2, ]
Chao1
#> KO1 KO2 KO3 KO4 KO5 KO6 OE1 OE2
#> 2446.667 2402.183 2146.868 2198.915 2056.470 2275.086 2483.564 2451.599
#> OE3 OE4 OE5 OE6 WT1 WT2 WT3 WT4
#> 2415.626 2496.515 2421.351 2515.467 2443.619 2214.343 2499.603 2420.341
#> WT5 WT6
#> 2306.519 2450.253
# ACE
ACE <- estimateR(otu.alp)[4, ]
ACE
#> KO1 KO2 KO3 KO4 KO5 KO6 OE1 OE2
#> 2443.701 2412.864 2137.443 2216.835 2095.769 2282.443 2477.663 2465.377
#> OE3 OE4 OE5 OE6 WT1 WT2 WT3 WT4
#> 2440.045 2509.310 2448.091 2511.687 2458.444 2247.837 2517.636 2433.983
#> WT5 WT6
#> 2290.245 2448.355
# Shannon
Shannon <- vegan::diversity(otu.alp, index = 'shannon', base = exp(1))
Shannon <- vegan::diversity(otu.alp, index = 'shannon', base = 2)
Shannon
#> KO1 KO2 KO3 KO4 KO5 KO6 OE1 OE2
#> 8.711013 8.760437 8.248617 8.276678 7.872175 8.594413 9.330282 8.963437
#> OE3 OE4 OE5 OE6 WT1 WT2 WT3 WT4
#> 8.619394 8.781839 8.919989 9.299840 8.431988 8.114854 8.933171 8.656702
#> WT5 WT6
#> 8.258683 8.763902
# Simpson1
Gini_simpson <-vegan:: diversity(otu.alp, index = 'simpson')
Gini_simpson
#> KO1 KO2 KO3 KO4 KO5 KO6 OE1 OE2
#> 0.9897551 0.9913117 0.9881726 0.9889146 0.9847379 0.9914342 0.9947090 0.9907655
#> OE3 OE4 OE5 OE6 WT1 WT2 WT3 WT4
#> 0.9892134 0.9919022 0.9930089 0.9948408 0.9884747 0.9857406 0.9924871 0.9907335
#> WT5 WT6
#> 0.9883208 0.9914640
#Simpson2
simpson_index <- 1 - Gini_simpson
# goods_coverage
goods_coverage <- 1 - rowSums(otu.alp == 1) / rowSums(otu.alp)
goods_coverage
#> KO1 KO2 KO3 KO4 KO5 KO6 OE1 OE2
#> 0.9857003 0.9857952 0.9859217 0.9852574 0.9849410 0.9860799 0.9894018 0.9882628
#> OE3 OE4 OE5 OE6 WT1 WT2 WT3 WT4
#> 0.9866494 0.9850359 0.9870923 0.9885476 0.9843715 0.9856370 0.9859850 0.9852890
#> WT5 WT6
#> 0.9853839 0.9859534
# PD
library(picante)
library(ape)
PD_whole_tree <- pd(otu, tree, include.root = FALSE)[1]
PD_whole_treeCode 3B（Example 14）
library(vegan)
library(MASS)
otu = read.delim("./data/otutab.txt",row.names = 1)
env = read.delim("./data/env.txt",row.names = 1)
map = read.delim("./data/metadata.tsv",row.names = 1)
head(otu)
# DCA
DCA<- decorana(otu)
# CCA
CCA <- cca(t(otu),env,scale = T)
# NMDS
dist <-vegdist(t(otu),method = "bray")
nmds_dist <- metaMDS(dist,k =2)
#> Run 0 stress 0.1212862
#> Run 1 stress 0.3718423
#> Run 2 stress 0.3719188
#> Run 3 stress 0.1227278
#> Run 4 stress 0.1287983
#> Run 5 stress 0.1374277
#> Run 6 stress 0.1287983
#> Run 7 stress 0.1354084
#> Run 8 stress 0.1212862
#> ... New best solution
#> ... Procrustes: rmse 0.0002278999 max resid 0.0006142734
#> ... Similar to previous best
#> Run 9 stress 0.1425687
#> Run 10 stress 0.1212864
#> ... Procrustes: rmse 0.0002616411 max resid 0.000694355
#> ... Similar to previous best
#> Run 11 stress 0.1354083
#> Run 12 stress 0.1212863
#> ... Procrustes: rmse 4.655722e-05 max resid 0.0001295647
#> ... Similar to previous best
#> Run 13 stress 0.1212862
#> ... New best solution
#> ... Procrustes: rmse 1.276057e-05 max resid 2.329946e-05
#> ... Similar to previous best
#> Run 14 stress 0.1347081
#> Run 15 stress 0.1418366
#> Run 16 stress 0.1559676
#> Run 17 stress 0.122728
#> Run 18 stress 0.1266971
#> Run 19 stress 0.1425688
#> Run 20 stress 0.1287983
#> *** Best solution repeated 1 times
# PCOA
dist= vegdist(t(otu),method = "bray")
PCOA= pcoa(dist,correction = "cailliez")
# LDA
data = t(otu)
# head(data)
data = as.data.frame(data)
# data$ID = row.names(data)
data = scale(data, center = TRUE, scale = TRUE)
model = MASS::lda(data, map$Group)
# VEGDIST
Vegan.dist = vegdist(t(otu),method = "bray")
# DIST
library(stats)
DIST<- dist(t(otu),method = "euclidean")Code 3D（Example 15）
# BiocManager::install("factoextra")
# HCLUST
dist<- dist(t(otu),method = "euclidean" )
otu.hclust <- hclust(dist, method = "complete")
# CLUSTER
library(survival)
CLUSTER<- cluster(t(otu))
# KMEANS
library(factoextra)
library(cluster)
set.seed(1)
gap = clusGap(scale(t(otu)),FUN = kmeans,nstart = 25,K.max = 10,B = 50)
fviz_gap_stat(gap)
KM<- kmeans(scale(t(otu)),centers =3 , nstart = 25)microbial community difference analysis
Code 4A（Example 16）
library(vegan)
library(tidyverse)
library(ade4)
otu = read.delim("./data/otutab.txt",row.names = 1)
map= read.delim("./data/metadata.tsv",row.names = 1)
unif <- dist <-vegdist(t(otu),method = "bray")
# ADONIS
ado = vegan:: adonis2( unif~ map$Group,method = "bray", by = NULL)
ado# ANOSIM
dat.ano = vegan::anosim(unif, map$Group)
dat.ano
#>
#> Call:
#> vegan::anosim(x = unif, grouping = map$Group)
#> Dissimilarity: bray
#>
#> ANOSIM statistic R: 0.5469
#> Significance: 0.001
#>
#> Permutation: free
#> Number of permutations: 999
# MRPP
mrpp = vegan::mrpp(unif, map$Group)
mrpp
#>
#> Call:
#> vegan::mrpp(dat = unif, grouping = map$Group)
#>
#> Dissimilarity index: bray
#> Weights for groups: n
#>
#> Class means and counts:
#>
#> KO OE WT
#> delta 0.3182 0.2747 0.2627
#> n 6 6 6
#>
#> Chance corrected within-group agreement A: 0.09649
#> Based on observed delta 0.2852 and expected delta 0.3157
#>
#> Significance of delta: 0.001
#> Permutation: free
#> Number of permutations: 999
# MANTEL
dist <-
otu %>% t() %>%
vegan::vegdist(method="bray") %>%
as.matrix()
gru = map[,"Group"] %>% unlist() %>% as.vector()
id = combn(unique(gru),2)
i = 1
id_dist <- row.names(map)[gru == id[1,i]]
dist1 = dist[id_dist,id_dist]
id_dist <- row.names(map)[gru == id[2,i]]
id_dist = id_dist[1:nrow(dist1)]
dist2 = dist[id_dist,id_dist]
vegan::mantel(dist1,dist2,method = "spearman")
#>
#> Mantel statistic based on Spearman's rank correlation rho
#>
#> Call:
#> vegan::mantel(xdis = dist1, ydis = dist2, method = "spearman")
#>
#> Mantel statistic r: 0.03571
#> Significance: 0.44861
#>
#> Upper quantiles of permutations (null model):
#> 90% 95% 97.5% 99%
#> 0.361 0.462 0.554 0.652
#> Permutation: free
#> Number of permutations: 719Code 4B（Example 17）
library(ggClusterNet)
library(phyloseq)
library(tidyverse)
data(ps)
#---wilcox.test
map= sample_data(ps)
i = 1
id.g = map$Group %>% unique() %>% as.character() %>% combn(2)
ASV_table = ps %>%
scale_micro(method = "sampling") %>%
subset_samples.wt("Group", id.g[,i]) %>%
vegan_otu() %>%
t() %>%
as.data.frame()
groupings <- ps %>%
subset_samples(Group %in% id.g[,i]) %>%
sample_data()
groupings$ID = row.names(groupings)
pvals <- apply(ASV_table, 1, function(x) wilcox.test(x ~ groupings$Group, exact=F)$p.value)
dat <- pvals %>% as.data.frame()
head(dat)colnames(dat) = "p"
tab.d12 = dat %>%
rownames_to_column(var = "id") %>%
dplyr::select(id,p) %>%
dplyr::filter( p < 0.05) %>%
dplyr::rename(
OTU = id
# p = p
) %>%
dplyr::mutate(group = " wilcox.test.rare")
head(tab.d12)Code 4C（Example 18）
map= sample_data(ps)
# head(map)
id.g = map$Group %>% unique() %>% as.character() %>% combn(2)
ASV_table = ps %>%
scale_micro(method = "sampling") %>%
subset_samples.wt("Group", id.g[,i]) %>%
vegan_otu() %>%
t() %>%
as.data.frame()
groupings <- ps %>%
subset_samples.wt("Group", id.g[,i]) %>%
sample_data()
groupings$ID = row.names(groupings)
pvals <- apply(ASV_table, 1, function(x) t.test(x ~ groupings$Group, exact=F)$p.value)
dat <- pvals %>% as.data.frame()
head(dat)colnames(dat) = "p"
tab.d11 = dat %>%
rownames_to_column(var = "id") %>%
dplyr::select(id,p) %>%
dplyr::filter( p < 0.05) %>%
dplyr::rename(
OTU = id
# p = p
) %>%
dplyr::mutate(group = "t.test.rare")
head(tab.d11)Code 4D（Example 19）
# BiocManager::install("edgeR")
library(edgeR)
phyloseq_to_edgeR = function(physeq, group, method="RLE", ...){
require("edgeR")
require("phyloseq")
# Enforce orientation.
if( !taxa_are_rows(physeq) ){ physeq <- t(physeq) }
x = as(otu_table(physeq), "matrix")
# Add one to protect against overflow, log(0) issues.
x = x + 1
# Check `group` argument
if( identical(all.equal(length(group), 1), TRUE) & nsamples(physeq) > 1 ){
# Assume that group was a sample variable name (must be categorical)
group = get_variable(physeq, group)
}
# Define gene annotations (`genes`) as tax_table
taxonomy = tax_table(physeq, errorIfNULL=FALSE)
if( !is.null(taxonomy) ){
taxonomy = data.frame(as(taxonomy, "matrix"))
}
# Now turn into a DGEList
y = DGEList(counts=x, group=group, genes=taxonomy, remove.zeros = TRUE, ...)
# Calculate the normalization factors
z = calcNormFactors(y, method=method)
# Check for division by zero inside `calcNormFactors`
if( !all(is.finite(z$samples$norm.factors)) ){
stop("Something wrong with edgeR::calcNormFactors on this data,
non-finite $norm.factors, consider changing `method` argument")
}
# Estimate dispersions
return(estimateTagwiseDisp(estimateCommonDisp(z)))
}
phylo <- ps %>%
subset_samples(Group %in% id.g[,i])
test <- phyloseq_to_edgeR(physeq = phylo, group="Group")
et = exactTest(test)
tt = topTags(et, n=nrow(test$table), adjust.method="fdr", sort.by="PValue")
res <- tt@.Data[[1]]
head(res)tab.d4 = res %>%
rownames_to_column(var = "id") %>%
dplyr::select(id,FDR) %>%
dplyr::filter(FDR < 0.05) %>%
dplyr::rename(
OTU = id,
p = FDR
) %>%
dplyr::mutate(group = "edgeR")
head(tab.d4)Code 4E（Example 20）
# BiocManager::install("DESeq2")
library(DESeq2)
ASV_table = ps %>%
filter_taxa(function(x) sum(x ) > 0 , TRUE) %>%
subset_samples(Group %in% id.g[,i]) %>%
vegan_otu() %>% t() %>%
as.data.frame()
groupings <- ps %>%
subset_samples(Group%in% id.g[,i]) %>%
sample_data()
groupings$ID = row.names(groupings)
dds <- DESeq2::DESeqDataSetFromMatrix(countData = ASV_table,
colData=groupings,
design = ~ Group)
dds_res <- DESeq2::DESeq(dds, sfType = "poscounts")
res <- DESeq2::results(dds_res, tidy=TRUE, format="DataFrame")
rownames(res) <- res$row
res <- res[,-1]
head(res)tab.d5 = res %>%
rownames_to_column(var = "id") %>%
dplyr::select(id,padj) %>%
dplyr::filter(padj < 0.05) %>%
dplyr::rename(
OTU = id,
p = padj
) %>%
dplyr::mutate(group = "DESeq2")
head(tab.d5)Code 4F （Example 21）
# BiocManager::install("metagenomeSeq")
library(metagenomeSeq)
ASV_table = ps %>%
filter_taxa(function(x) sum(x ) > 0 , TRUE) %>%
subset_samples(Group %in% id.g[,i]) %>%
vegan_otu() %>% t() %>%
as.data.frame()
groupings <- ps %>%
subset_samples(Group%in% id.g[,i]) %>%
sample_data()
groupings$ID = row.names(groupings)
data_list <- list()
data_list[["counts"]] <- ASV_table
data_list[["taxa"]] <- rownames(ASV_table)
pheno <- AnnotatedDataFrame(groupings)
pheno
#> An object of class 'AnnotatedDataFrame'
#> rowNames: KO1 KO2 ... OE6 (12 total)
#> varLabels: Group Date ... ID (11 total)
#> varMetadata: labelDescription
counts <- AnnotatedDataFrame(ASV_table)
feature_data <- data.frame("ASV"=rownames(ASV_table),
"ASV2"=rownames(ASV_table))
feature_data <- AnnotatedDataFrame(feature_data)
rownames(feature_data) <- feature_data@data$ASV
test_obj <- newMRexperiment(counts = data_list$counts, phenoData = pheno, featureData = feature_data)
p <- cumNormStat(test_obj, pFlag = TRUE)
test_obj_norm <- cumNorm(test_obj, p=p)
fromula <- as.formula(paste(~1, "Group", sep=" + "))
pd <- pData(test_obj_norm)
mod <- model.matrix(fromula, data=pd)
regres <- fitFeatureModel(test_obj_norm, mod)
res_table <- MRfulltable(regres, number = length(rownames(ASV_table)))
head(res_table)
tab.d10 = res_table %>%
rownames_to_column(var = "id") %>%
dplyr::select(id,adjPvalues) %>%
dplyr::filter(adjPvalues < 0.05) %>%
dplyr::rename(
OTU = id,
p = adjPvalues
) %>%
dplyr::mutate(group = "metagenomeSeq")
head(tab.d10)Code 4G（Example 22）
# BiocManager::install("ALDEx2")
library(ALDEx2)
ASV_table = ps %>%
filter_taxa(function(x) sum(x ) > 0 , TRUE) %>%
subset_samples(Group %in% id.g[,i]) %>%
vegan_otu() %>% t() %>%
as.data.frame()
groupings <- ps %>%
subset_samples(Group%in% id.g[,i]) %>%
sample_data()
groupings$ID = row.names(groupings)
results <- ALDEx2::aldex(reads=ASV_table, conditions = groupings$Group,
mc.samples = 128,
test="t",
effect=TRUE,
include.sample.summary = FALSE,
verbose=TRUE,
denom="all")
#> |------------(25%)----------(50%)----------(75%)----------|
head(results)
tab.d1 = results %>%
as.data.frame() %>%
dplyr::filter(we.ep < 0.05) %>% rownames_to_column(var = "id") %>%
dplyr::select(id,we.ep) %>%
dplyr::rename(
OTU = id,
p = we.ep
) %>%
dplyr::mutate(group = "Aldex2")
head(tab.d1)Code 4H（Example 23）
i= 1
library(edgeR)
library(tidyverse)
library(ggClusterNet)
library(phyloseq)
ASV_table = ps %>%
filter_taxa(function(x) sum(x ) > 0 , TRUE) %>%
subset_samples(Group %in% id.g[,i]) %>%
vegan_otu() %>% t() %>%
as.data.frame()
groupings <- ps %>%
subset_samples(Group%in% id.g[,i]) %>%
sample_data()
groupings$ID = row.names(groupings)
DGE_LIST <- DGEList(ASV_table)
### do normalization
### Reference sample will be the sample with the highest read depth
### check if upper quartile method works for selecting reference
Upper_Quartile_norm_test <- edgeR::calcNormFactors(DGE_LIST,method="upperquartile")
summary_upper_quartile <- summary(Upper_Quartile_norm_test$samples$norm.factors)[3]
if(is.na(summary_upper_quartile) | is.infinite(summary_upper_quartile)){
message("Upper Quartile reference selection failed will use find sample with largest sqrt(read_depth) to use as reference")
Ref_col <- which.max(colSums(sqrt(ASV_table)))
DGE_LIST_Norm <- calcNormFactors(DGE_LIST, method = "TMM", refColumn = Ref_col)
fileConn<-file(args[[4]])
writeLines(c("Used max square root read depth to determine reference sample"), fileConn)
close(fileConn)
}else{
DGE_LIST_Norm <- edgeR::calcNormFactors(DGE_LIST, method="TMM")
}
## make matrix for testing
# colnames(groupings) <- c("comparison")
groupings = groupings %>% as.tibble() %>% as.data.frame()
mm <- model.matrix(~Group, groupings)
voomvoom <- voom(DGE_LIST_Norm, mm, plot=F)
fit <- lmFit(voomvoom,mm)
fit <- eBayes(fit)
res <- topTable(fit, coef=2, n=nrow(DGE_LIST_Norm), sort.by="none")
head(res)
tab.d7 = res %>%
rownames_to_column(var = "id") %>%
dplyr::select(id,adj.P.Val) %>%
dplyr::filter(adj.P.Val < 0.05) %>%
dplyr::rename(
OTU = id,
p = adj.P.Val
) %>%
dplyr::mutate(group = "limma.voom.TMM")
head(tab.d7)Code 4I（Example 24）
# remotes::install_github("FrederickHuangLin/ANCOMBC")
# BiocManager::install("ANCOMBC")
# BiocManager::install("microbiome")
library(ANCOMBC)
data(atlas1006, package = "microbiome")
tse = mia::makeTreeSummarizedExperimentFromPhyloseq(atlas1006)
# subset to baseline
tse = tse[, tse$time == 0]
# run ancombc function
set.seed(123)
out = ancombc(data = tse, assay_name = "counts",
tax_level = "Family", phyloseq = NULL,
formula = "age + nationality + bmi_group",
p_adj_method = "holm", prv_cut = 0.10, lib_cut = 1000,
group = "bmi_group", struc_zero = TRUE, neg_lb = FALSE,
tol = 1e-5, max_iter = 100, conserve = TRUE,
alpha = 0.05, global = TRUE, n_cl = 1, verbose = TRUE)
res_prim = out$res
res_global = out$res_global
# 旧版本包使用指南
# main_var <- colnames(groupings)[1]
# p_adj_method = "BH"
# alpha=0.05
# adj_formula=NULL
# rand_formula=NULL
#
# ?ancombc
#
# res <-ANCOMBC::ANCOMBC(feature_table = feature_table,
# meta_data = metadata,
# struc_zero = struc_zero,
# main_var = main_var,
# p_adj_method = p_adj_method,
# alpha=alpha, adj_formula = adj_formula,
# rand_formula = rand_formula
# )
#
# dat = res$out
# head(dat)
# dat$detected_0.6
# ANCOM.value = "detected_0.6"
# tab.d2 = dat %>% dplyr::select(taxa_id,detected_0.6) %>%
# dplyr::filter(detected_0.6 == TRUE) %>%
# dplyr::rename(
# OTU = taxa_id,
# p = detected_0.6
# ) %>%
# dplyr::mutate(group = "ANCOMII")Code 4J （Example 25）
# BiocManager::install("corncob")
library(corncob)
library(phyloseq)
library(ggClusterNet)
map= sample_data(ps)
# head(map)
id.g = map$Group %>% unique() %>% as.character() %>% combn(2)
i = 1
phylo <- ps %>%
subset_samples(Group %in% id.g[,i])
my_formula <- as.formula(paste("~","Group",sep=" ", collapse = ""))
my_formula
#> ~Group
results <- corncob::differentialTest(formula= my_formula,
phi.formula = my_formula,
phi.formula_null = my_formula,
formula_null = ~ 1,
test="Wald", data=phylo,
boot=F,
fdr_cutoff = 0.05)
dat = results$p_fdr %>% as.data.frame()
head(dat)colnames(dat) = "p_fdr"
dat$p = results$p
tab.d3 = dat %>%
rownames_to_column(var = "id") %>%
dplyr::select(id,p_fdr) %>%
dplyr::filter(p_fdr < 0.05) %>%
dplyr::rename(
OTU = id,
p = p_fdr
) %>%
dplyr::mutate(group = "corncob")
head(tab.d3)Code 4K（Example 26）
# BiocManager::install("Maaslin2")
ASV_table = ps %>%
filter_taxa(function(x) sum(x ) > 0 , TRUE) %>%
subset_samples(Group %in% id.g[,i]) %>%
vegan_otu() %>% t() %>%
as.data.frame()
groupings <- ps %>%
subset_samples(Group%in% id.g[,i]) %>%
sample_data()
groupings$ID = row.names(groupings)
library(Maaslin2)
ASV_table <- data.frame(t(ASV_table), check.rows = F, check.names = F, stringsAsFactors = F)
row.names(groupings) = groupings$ID
fit_data <- Maaslin2(
ASV_table, groupings,output = "./2.Microbial community analysis/Maaslin2", transform = "AST",
fixed_effects = "Group",
standardize = FALSE, plot_heatmap = FALSE, plot_scatter = FALSE)
#> [1] "Warning: Deleting existing log file: ./2.Microbial community analysis/Maaslin2/maaslin2.log"
#> 2023-05-08 20:11:39.662366 INFO::Writing function arguments to log file
#> 2023-05-08 20:11:39.680943 INFO::Verifying options selected are valid
#> 2023-05-08 20:11:39.711769 INFO::Determining format of input files
#> 2023-05-08 20:11:39.712871 INFO::Input format is data samples as rows and metadata samples as rows
#> 2023-05-08 20:11:39.746626 INFO::Formula for fixed effects: expr ~ Group
#> 2023-05-08 20:11:39.761199 INFO::Filter data based on min abundance and min prevalence
#> 2023-05-08 20:11:39.76229 INFO::Total samples in data: 12
#> 2023-05-08 20:11:39.763224 INFO::Min samples required with min abundance for a feature not to be filtered: 1.200000
#> 2023-05-08 20:11:39.781324 INFO::Total filtered features: 20
#> 2023-05-08 20:11:39.782647 INFO::Filtered feature names from abundance and prevalence filtering: ASV_2735, ASV_1039, ASV_2562, ASV_2918, ASV_2849, ASV_1481, ASV_2844, ASV_1373, ASV_2453, ASV_2920, ASV_1149, ASV_2483, ASV_2848, ASV_1479, ASV_2817, ASV_1038, ASV_1168, ASV_1313, ASV_2369, ASV_2900
#> 2023-05-08 20:11:39.816424 INFO::Total filtered features with variance filtering: 0
#> 2023-05-08 20:11:39.817755 INFO::Filtered feature names from variance filtering:
#> 2023-05-08 20:11:39.818727 INFO::Running selected normalization method: TSS
#> 2023-05-08 20:11:39.835157 INFO::Bypass z-score application to metadata
#> 2023-05-08 20:11:39.836205 INFO::Running selected transform method: AST
#> 2023-05-08 20:11:39.856869 INFO::Running selected analysis method: LM
#> 2023-05-08 20:11:39.862571 INFO::Fitting model to feature number 1, ASV_1
#> 2023-05-08 20:11:39.865697 INFO::Fitting model to feature number 2, ASV_1591
#> 2023-05-08 20:11:39.868148 INFO::Fitting model to feature number 3, ASV_657
#> 2023-05-08 20:11:39.870788 INFO::Fitting model to feature number 4, ASV_28
#> 2023-05-08 20:11:39.87341 INFO::Fitting model to feature number 5, ASV_1717
#> 2023-05-08 20:11:39.875788 INFO::Fitting model to feature number 6, ASV_2407
#> 2023-05-08 20:11:39.87822 INFO::Fitting model to feature number 7, ASV_1480
#> 2023-05-08 20:11:39.880614 INFO::Fitting model to feature number 8, ASV_125
#> 2023-05-08 20:11:39.88325 INFO::Fitting model to feature number 9, ASV_1960
#> 2023-05-08 20:11:39.885791 INFO::Fitting model to feature number 10, ASV_135
#> 2023-05-08 20:11:39.888179 INFO::Fitting model to feature number 11, ASV_1418
#> 2023-05-08 20:11:39.89103 INFO::Fitting model to feature number 12, ASV_434
#> 2023-05-08 20:11:39.893852 INFO::Fitting model to feature number 13, ASV_1109
#> 2023-05-08 20:11:39.896467 INFO::Fitting model to feature number 14, ASV_688
#> 2023-05-08 20:11:39.898958 INFO::Fitting model to feature number 15, ASV_1604
#> 2023-05-08 20:11:39.901371 INFO::Fitting model to feature number 16, ASV_2410
#> 2023-05-08 20:11:39.903975 INFO::Fitting model to feature number 17, ASV_2539
#> 2023-05-08 20:11:39.906855 INFO::Fitting model to feature number 18, ASV_1493
#> 2023-05-08 20:11:39.909374 INFO::Fitting model to feature number 19, ASV_1087
#> 2023-05-08 20:11:39.911741 INFO::Fitting model to feature number 20, ASV_2789
#> 2023-05-08 20:11:39.914105 INFO::Fitting model to feature number 21, ASV_8
#> 2023-05-08 20:11:39.917126 INFO::Fitting model to feature number 22, ASV_174
#> 2023-05-08 20:11:39.919661 INFO::Fitting model to feature number 23, ASV_208
#> 2023-05-08 20:11:39.922515 INFO::Fitting model to feature number 24, ASV_716
#> 2023-05-08 20:11:39.925366 INFO::Fitting model to feature number 25, ASV_1181
#> 2023-05-08 20:11:39.928179 INFO::Fitting model to feature number 26, ASV_1312
#> 2023-05-08 20:11:39.93084 INFO::Fitting model to feature number 27, ASV_1464
#> 2023-05-08 20:11:39.933344 INFO::Fitting model to feature number 28, ASV_1703
#> 2023-05-08 20:11:39.936057 INFO::Fitting model to feature number 29, ASV_1667
#> 2023-05-08 20:11:39.938904 INFO::Fitting model to feature number 30, ASV_1225
#> 2023-05-08 20:11:39.941573 INFO::Fitting model to feature number 31, ASV_106
#> 2023-05-08 20:11:39.944144 INFO::Fitting model to feature number 32, ASV_1287
#> 2023-05-08 20:11:39.946656 INFO::Fitting model to feature number 33, ASV_2611
#> 2023-05-08 20:11:39.949061 INFO::Fitting model to feature number 34, ASV_2618
#> 2023-05-08 20:11:39.951639 INFO::Fitting model to feature number 35, ASV_266
#> 2023-05-08 20:11:39.955282 INFO::Fitting model to feature number 36, ASV_1351
#> 2023-05-08 20:11:39.95784 INFO::Fitting model to feature number 37, ASV_1113
#> 2023-05-08 20:11:39.96049 INFO::Fitting model to feature number 38, ASV_340
#> 2023-05-08 20:11:39.962989 INFO::Fitting model to feature number 39, ASV_1268
#> 2023-05-08 20:11:39.965489 INFO::Fitting model to feature number 40, ASV_1296
#> 2023-05-08 20:11:39.968171 INFO::Fitting model to feature number 41, ASV_16
#> 2023-05-08 20:11:39.971026 INFO::Fitting model to feature number 42, ASV_443
#> 2023-05-08 20:11:39.973961 INFO::Fitting model to feature number 43, ASV_479
#> 2023-05-08 20:11:39.976639 INFO::Fitting model to feature number 44, ASV_159
#> 2023-05-08 20:11:39.979225 INFO::Fitting model to feature number 45, ASV_484
#> 2023-05-08 20:11:39.981715 INFO::Fitting model to feature number 46, ASV_666
#> 2023-05-08 20:11:39.984334 INFO::Fitting model to feature number 47, ASV_414
#> 2023-05-08 20:11:39.986987 INFO::Fitting model to feature number 48, ASV_1757
#> 2023-05-08 20:11:39.989484 INFO::Fitting model to feature number 49, ASV_314
#> 2023-05-08 20:11:39.9921 INFO::Fitting model to feature number 50, ASV_397
#> 2023-05-08 20:11:39.994665 INFO::Fitting model to feature number 51, ASV_1574
#> 2023-05-08 20:11:39.99716 INFO::Fitting model to feature number 52, ASV_808
#> 2023-05-08 20:11:39.99988 INFO::Fitting model to feature number 53, ASV_1700
#> 2023-05-08 20:11:40.002584 INFO::Fitting model to feature number 54, ASV_1292
#> 2023-05-08 20:11:40.005394 INFO::Fitting model to feature number 55, ASV_1953
#> 2023-05-08 20:11:40.00835 INFO::Fitting model to feature number 56, ASV_2
#> 2023-05-08 20:11:40.011007 INFO::Fitting model to feature number 57, ASV_97
#> 2023-05-08 20:11:40.013765 INFO::Fitting model to feature number 58, ASV_350
#> 2023-05-08 20:11:40.017059 INFO::Fitting model to feature number 59, ASV_1061
#> 2023-05-08 20:11:40.020128 INFO::Fitting model to feature number 60, ASV_1580
#> 2023-05-08 20:11:40.022948 INFO::Fitting model to feature number 61, ASV_2584
#> 2023-05-08 20:11:40.025613 INFO::Fitting model to feature number 62, ASV_145
#> 2023-05-08 20:11:40.02822 INFO::Fitting model to feature number 63, ASV_237
#> 2023-05-08 20:11:40.031674 INFO::Fitting model to feature number 64, ASV_1046
#> 2023-05-08 20:11:40.03451 INFO::Fitting model to feature number 65, ASV_2719
#> 2023-05-08 20:11:40.037198 INFO::Fitting model to feature number 66, ASV_1092
#> 2023-05-08 20:11:40.039758 INFO::Fitting model to feature number 67, ASV_358
#> 2023-05-08 20:11:40.042357 INFO::Fitting model to feature number 68, ASV_1285
#> 2023-05-08 20:11:40.044862 INFO::Fitting model to feature number 69, ASV_1935
#> 2023-05-08 20:11:40.047511 INFO::Fitting model to feature number 70, ASV_1163
#> 2023-05-08 20:11:40.050008 INFO::Fitting model to feature number 71, ASV_79
#> 2023-05-08 20:11:40.052804 INFO::Fitting model to feature number 72, ASV_2511
#> 2023-05-08 20:11:40.055346 INFO::Fitting model to feature number 73, ASV_2574
#> 2023-05-08 20:11:40.057817 INFO::Fitting model to feature number 74, ASV_167
#> 2023-05-08 20:11:40.060286 INFO::Fitting model to feature number 75, ASV_234
#> 2023-05-08 20:11:40.063231 INFO::Fitting model to feature number 76, ASV_30
#> 2023-05-08 20:11:40.065918 INFO::Fitting model to feature number 77, ASV_44
#> 2023-05-08 20:11:40.068456 INFO::Fitting model to feature number 78, ASV_293
#> 2023-05-08 20:11:40.070972 INFO::Fitting model to feature number 79, ASV_324
#> 2023-05-08 20:11:40.073671 INFO::Fitting model to feature number 80, ASV_32
#> 2023-05-08 20:11:40.076955 INFO::Fitting model to feature number 81, ASV_111
#> 2023-05-08 20:11:40.079755 INFO::Fitting model to feature number 82, ASV_595
#> 2023-05-08 20:11:40.082541 INFO::Fitting model to feature number 83, ASV_128
#> 2023-05-08 20:11:40.085345 INFO::Fitting model to feature number 84, ASV_406
#> 2023-05-08 20:11:40.087976 INFO::Fitting model to feature number 85, ASV_132
#> 2023-05-08 20:11:40.090745 INFO::Fitting model to feature number 86, ASV_640
#> 2023-05-08 20:11:40.093646 INFO::Fitting model to feature number 87, ASV_709
#> 2023-05-08 20:11:40.096857 INFO::Fitting model to feature number 88, ASV_748
#> 2023-05-08 20:11:40.099845 INFO::Fitting model to feature number 89, ASV_2361
#> 2023-05-08 20:11:40.103193 INFO::Fitting model to feature number 90, ASV_3
#> 2023-05-08 20:11:40.10607 INFO::Fitting model to feature number 91, ASV_4
#> 2023-05-08 20:11:40.108998 INFO::Fitting model to feature number 92, ASV_55
#> 2023-05-08 20:11:40.111833 INFO::Fitting model to feature number 93, ASV_547
#> 2023-05-08 20:11:40.114508 INFO::Fitting model to feature number 94, ASV_136
#> 2023-05-08 20:11:40.11732 INFO::Fitting model to feature number 95, ASV_605
#> 2023-05-08 20:11:40.120257 INFO::Fitting model to feature number 96, ASV_6
#> 2023-05-08 20:11:40.123155 INFO::Fitting model to feature number 97, ASV_17
#> 2023-05-08 20:11:40.126142 INFO::Fitting model to feature number 98, ASV_39
#> 2023-05-08 20:11:40.128881 INFO::Fitting model to feature number 99, ASV_60
#> 2023-05-08 20:11:40.13155 INFO::Fitting model to feature number 100, ASV_253
#> 2023-05-08 20:11:40.134371 INFO::Fitting model to feature number 101, ASV_192
#> 2023-05-08 20:11:40.137057 INFO::Fitting model to feature number 102, ASV_14
#> 2023-05-08 20:11:40.139812 INFO::Fitting model to feature number 103, ASV_127
#> 2023-05-08 20:11:40.14258 INFO::Fitting model to feature number 104, ASV_166
#> 2023-05-08 20:11:40.14519 INFO::Fitting model to feature number 105, ASV_212
#> 2023-05-08 20:11:40.147813 INFO::Fitting model to feature number 106, ASV_1986
#> 2023-05-08 20:11:40.150385 INFO::Fitting model to feature number 107, ASV_2062
#> 2023-05-08 20:11:40.152999 INFO::Fitting model to feature number 108, ASV_150
#> 2023-05-08 20:11:40.178927 INFO::Fitting model to feature number 109, ASV_619
#> 2023-05-08 20:11:40.182741 INFO::Fitting model to feature number 110, ASV_1476
#> 2023-05-08 20:11:40.186714 INFO::Fitting model to feature number 111, ASV_973
#> 2023-05-08 20:11:40.190086 INFO::Fitting model to feature number 112, ASV_2250
#> 2023-05-08 20:11:40.19294 INFO::Fitting model to feature number 113, ASV_315
#> 2023-05-08 20:11:40.195665 INFO::Fitting model to feature number 114, ASV_2461
#> 2023-05-08 20:11:40.198386 INFO::Fitting model to feature number 115, ASV_420
#> 2023-05-08 20:11:40.20115 INFO::Fitting model to feature number 116, ASV_1072
#> 2023-05-08 20:11:40.204347 INFO::Fitting model to feature number 117, ASV_446
#> 2023-05-08 20:11:40.207264 INFO::Fitting model to feature number 118, ASV_1425
#> 2023-05-08 20:11:40.209927 INFO::Fitting model to feature number 119, ASV_526
#> 2023-05-08 20:11:40.212506 INFO::Fitting model to feature number 120, ASV_2543
#> 2023-05-08 20:11:40.215071 INFO::Fitting model to feature number 121, ASV_94
#> 2023-05-08 20:11:40.217933 INFO::Fitting model to feature number 122, ASV_220
#> 2023-05-08 20:11:40.220733 INFO::Fitting model to feature number 123, ASV_593
#> 2023-05-08 20:11:40.223543 INFO::Fitting model to feature number 124, ASV_863
#> 2023-05-08 20:11:40.226333 INFO::Fitting model to feature number 125, ASV_107
#> 2023-05-08 20:11:40.229148 INFO::Fitting model to feature number 126, ASV_123
#> 2023-05-08 20:11:40.232099 INFO::Fitting model to feature number 127, ASV_600
#> 2023-05-08 20:11:40.235164 INFO::Fitting model to feature number 128, ASV_1661
#> 2023-05-08 20:11:40.237922 INFO::Fitting model to feature number 129, ASV_1024
#> 2023-05-08 20:11:40.24066 INFO::Fitting model to feature number 130, ASV_1278
#> 2023-05-08 20:11:40.243403 INFO::Fitting model to feature number 131, ASV_2442
#> 2023-05-08 20:11:40.246267 INFO::Fitting model to feature number 132, ASV_2856
#> 2023-05-08 20:11:40.250319 INFO::Fitting model to feature number 133, ASV_100
#> 2023-05-08 20:11:40.253526 INFO::Fitting model to feature number 134, ASV_330
#> 2023-05-08 20:11:40.256378 INFO::Fitting model to feature number 135, ASV_328
#> 2023-05-08 20:11:40.259241 INFO::Fitting model to feature number 136, ASV_421
#> 2023-05-08 20:11:40.262069 INFO::Fitting model to feature number 137, ASV_1911
#> 2023-05-08 20:11:40.265356 INFO::Fitting model to feature number 138, ASV_538
#> 2023-05-08 20:11:40.268643 INFO::Fitting model to feature number 139, ASV_1062
#> 2023-05-08 20:11:40.271814 INFO::Fitting model to feature number 140, ASV_2032
#> 2023-05-08 20:11:40.275373 INFO::Fitting model to feature number 141, ASV_1315
#> 2023-05-08 20:11:40.278295 INFO::Fitting model to feature number 142, ASV_810
#> 2023-05-08 20:11:40.281222 INFO::Fitting model to feature number 143, ASV_287
#> 2023-05-08 20:11:40.284173 INFO::Fitting model to feature number 144, ASV_1280
#> 2023-05-08 20:11:40.287155 INFO::Fitting model to feature number 145, ASV_783
#> 2023-05-08 20:11:40.289958 INFO::Fitting model to feature number 146, ASV_2371
#> 2023-05-08 20:11:40.29291 INFO::Fitting model to feature number 147, ASV_1747
#> 2023-05-08 20:11:40.295533 INFO::Fitting model to feature number 148, ASV_1254
#> 2023-05-08 20:11:40.298622 INFO::Fitting model to feature number 149, ASV_636
#> 2023-05-08 20:11:40.301334 INFO::Fitting model to feature number 150, ASV_809
#> 2023-05-08 20:11:40.304062 INFO::Fitting model to feature number 151, ASV_1206
#> 2023-05-08 20:11:40.306745 INFO::Fitting model to feature number 152, ASV_2140
#> 2023-05-08 20:11:40.309512 INFO::Fitting model to feature number 153, ASV_2896
#> 2023-05-08 20:11:40.312709 INFO::Fitting model to feature number 154, ASV_2800
#> 2023-05-08 20:11:40.315618 INFO::Fitting model to feature number 155, ASV_697
#> 2023-05-08 20:11:40.318461 INFO::Fitting model to feature number 156, ASV_1089
#> 2023-05-08 20:11:40.321284 INFO::Fitting model to feature number 157, ASV_2671
#> 2023-05-08 20:11:40.324014 INFO::Fitting model to feature number 158, ASV_1275
#> 2023-05-08 20:11:40.326737 INFO::Fitting model to feature number 159, ASV_2417
#> 2023-05-08 20:11:40.330671 INFO::Fitting model to feature number 160, ASV_721
#> 2023-05-08 20:11:40.33406 INFO::Fitting model to feature number 161, ASV_11
#> 2023-05-08 20:11:40.337295 INFO::Fitting model to feature number 162, ASV_57
#> 2023-05-08 20:11:40.340405 INFO::Fitting model to feature number 163, ASV_61
#> 2023-05-08 20:11:40.343749 INFO::Fitting model to feature number 164, ASV_2502
#> 2023-05-08 20:11:40.346658 INFO::Fitting model to feature number 165, ASV_144
#> 2023-05-08 20:11:40.349455 INFO::Fitting model to feature number 166, ASV_169
#> 2023-05-08 20:11:40.352201 INFO::Fitting model to feature number 167, ASV_36
#> 2023-05-08 20:11:40.354883 INFO::Fitting model to feature number 168, ASV_38
#> 2023-05-08 20:11:40.357637 INFO::Fitting model to feature number 169, ASV_83
#> 2023-05-08 20:11:40.360639 INFO::Fitting model to feature number 170, ASV_50
#> 2023-05-08 20:11:40.363366 INFO::Fitting model to feature number 171, ASV_99
#> 2023-05-08 20:11:40.366105 INFO::Fitting model to feature number 172, ASV_418
#> 2023-05-08 20:11:40.368838 INFO::Fitting model to feature number 173, ASV_1686
#> 2023-05-08 20:11:40.371798 INFO::Fitting model to feature number 174, ASV_2335
#> 2023-05-08 20:11:40.374807 INFO::Fitting model to feature number 175, ASV_41
#> 2023-05-08 20:11:40.37794 INFO::Fitting model to feature number 176, ASV_187
#> 2023-05-08 20:11:40.381277 INFO::Fitting model to feature number 177, ASV_73
#> 2023-05-08 20:11:40.384413 INFO::Fitting model to feature number 178, ASV_115
#> 2023-05-08 20:11:40.387355 INFO::Fitting model to feature number 179, ASV_614
#> 2023-05-08 20:11:40.390351 INFO::Fitting model to feature number 180, ASV_85
#> 2023-05-08 20:11:40.393193 INFO::Fitting model to feature number 181, ASV_98
#> 2023-05-08 20:11:40.396014 INFO::Fitting model to feature number 182, ASV_516
#> 2023-05-08 20:11:40.398729 INFO::Fitting model to feature number 183, ASV_1386
#> 2023-05-08 20:11:40.401525 INFO::Fitting model to feature number 184, ASV_390
#> 2023-05-08 20:11:40.40431 INFO::Fitting model to feature number 185, ASV_1004
#> 2023-05-08 20:11:40.408267 INFO::Fitting model to feature number 186, ASV_596
#> 2023-05-08 20:11:40.411852 INFO::Fitting model to feature number 187, ASV_780
#> 2023-05-08 20:11:40.415129 INFO::Fitting model to feature number 188, ASV_1729
#> 2023-05-08 20:11:40.418292 INFO::Fitting model to feature number 189, ASV_1832
#> 2023-05-08 20:11:40.421628 INFO::Fitting model to feature number 190, ASV_2173
#> 2023-05-08 20:11:40.424911 INFO::Fitting model to feature number 191, ASV_652
#> 2023-05-08 20:11:40.428247 INFO::Fitting model to feature number 192, ASV_945
#> 2023-05-08 20:11:40.431241 INFO::Fitting model to feature number 193, ASV_1920
#> 2023-05-08 20:11:40.434176 INFO::Fitting model to feature number 194, ASV_2294
#> 2023-05-08 20:11:40.438356 INFO::Fitting model to feature number 195, ASV_1362
#> 2023-05-08 20:11:40.441319 INFO::Fitting model to feature number 196, ASV_141
#> 2023-05-08 20:11:40.444185 INFO::Fitting model to feature number 197, ASV_251
#> 2023-05-08 20:11:40.447178 INFO::Fitting model to feature number 198, ASV_175
#> 2023-05-08 20:11:40.450188 INFO::Fitting model to feature number 199, ASV_201
#> 2023-05-08 20:11:40.453336 INFO::Fitting model to feature number 200, ASV_1008
#> 2023-05-08 20:11:40.457087 INFO::Fitting model to feature number 201, ASV_185
#> 2023-05-08 20:11:40.460191 INFO::Fitting model to feature number 202, ASV_537
#> 2023-05-08 20:11:40.463192 INFO::Fitting model to feature number 203, ASV_1003
#> 2023-05-08 20:11:40.466077 INFO::Fitting model to feature number 204, ASV_432
#> 2023-05-08 20:11:40.469203 INFO::Fitting model to feature number 205, ASV_683
#> 2023-05-08 20:11:40.47214 INFO::Fitting model to feature number 206, ASV_877
#> 2023-05-08 20:11:40.474934 INFO::Fitting model to feature number 207, ASV_1671
#> 2023-05-08 20:11:40.477708 INFO::Fitting model to feature number 208, ASV_1812
#> 2023-05-08 20:11:40.48068 INFO::Fitting model to feature number 209, ASV_338
#> 2023-05-08 20:11:40.484879 INFO::Fitting model to feature number 210, ASV_920
#> 2023-05-08 20:11:40.48819 INFO::Fitting model to feature number 211, ASV_2181
#> 2023-05-08 20:11:40.491339 INFO::Fitting model to feature number 212, ASV_935
#> 2023-05-08 20:11:40.494249 INFO::Fitting model to feature number 213, ASV_2587
#> 2023-05-08 20:11:40.497397 INFO::Fitting model to feature number 214, ASV_710
#> 2023-05-08 20:11:40.500502 INFO::Fitting model to feature number 215, ASV_1982
#> 2023-05-08 20:11:40.503488 INFO::Fitting model to feature number 216, ASV_1241
#> 2023-05-08 20:11:40.50626 INFO::Fitting model to feature number 217, ASV_2199
#> 2023-05-08 20:11:40.509039 INFO::Fitting model to feature number 218, ASV_2607
#> 2023-05-08 20:11:40.512023 INFO::Fitting model to feature number 219, ASV_2919
#> 2023-05-08 20:11:40.515332 INFO::Fitting model to feature number 220, ASV_391
#> 2023-05-08 20:11:40.518346 INFO::Fitting model to feature number 221, ASV_2288
#> 2023-05-08 20:11:40.521167 INFO::Fitting model to feature number 222, ASV_1277
#> 2023-05-08 20:11:40.523926 INFO::Fitting model to feature number 223, ASV_2404
#> 2023-05-08 20:11:40.526689 INFO::Fitting model to feature number 224, ASV_2202
#> 2023-05-08 20:11:40.529731 INFO::Fitting model to feature number 225, ASV_2055
#> 2023-05-08 20:11:40.532505 INFO::Fitting model to feature number 226, ASV_534
#> 2023-05-08 20:11:40.535632 INFO::Fitting model to feature number 227, ASV_978
#> 2023-05-08 20:11:40.538681 INFO::Fitting model to feature number 228, ASV_2796
#> 2023-05-08 20:11:40.541752 INFO::Fitting model to feature number 229, ASV_228
#> 2023-05-08 20:11:40.544714 INFO::Fitting model to feature number 230, ASV_904
#> 2023-05-08 20:11:40.547662 INFO::Fitting model to feature number 231, ASV_1017
#> 2023-05-08 20:11:40.550534 INFO::Fitting model to feature number 232, ASV_527
#> 2023-05-08 20:11:40.553291 INFO::Fitting model to feature number 233, ASV_757
#> 2023-05-08 20:11:40.556072 INFO::Fitting model to feature number 234, ASV_953
#> 2023-05-08 20:11:40.558875 INFO::Fitting model to feature number 235, ASV_1343
#> 2023-05-08 20:11:40.562168 INFO::Fitting model to feature number 236, ASV_1553
#> 2023-05-08 20:11:40.565259 INFO::Fitting model to feature number 237, ASV_1672
#> 2023-05-08 20:11:40.569016 INFO::Fitting model to feature number 238, ASV_1997
#> 2023-05-08 20:11:40.571884 INFO::Fitting model to feature number 239, ASV_2727
#> 2023-05-08 20:11:40.574713 INFO::Fitting model to feature number 240, ASV_1222
#> 2023-05-08 20:11:40.577895 INFO::Fitting model to feature number 241, ASV_1487
#> 2023-05-08 20:11:40.580803 INFO::Fitting model to feature number 242, ASV_1579
#> 2023-05-08 20:11:40.583868 INFO::Fitting model to feature number 243, ASV_1413
#> 2023-05-08 20:11:40.58682 INFO::Fitting model to feature number 244, ASV_2226
#> 2023-05-08 20:11:40.589891 INFO::Fitting model to feature number 245, ASV_1810
#> 2023-05-08 20:11:40.593245 INFO::Fitting model to feature number 246, ASV_2121
#> 2023-05-08 20:11:40.596254 INFO::Fitting model to feature number 247, ASV_2206
#> 2023-05-08 20:11:40.599112 INFO::Fitting model to feature number 248, ASV_603
#> 2023-05-08 20:11:40.601937 INFO::Fitting model to feature number 249, ASV_1585
#> 2023-05-08 20:11:40.604713 INFO::Fitting model to feature number 250, ASV_1097
#> 2023-05-08 20:11:40.607656 INFO::Fitting model to feature number 251, ASV_2549
#> 2023-05-08 20:11:40.610472 INFO::Fitting model to feature number 252, ASV_1397
#> 2023-05-08 20:11:40.613277 INFO::Fitting model to feature number 253, ASV_2107
#> 2023-05-08 20:11:40.616071 INFO::Fitting model to feature number 254, ASV_2298
#> 2023-05-08 20:11:40.618878 INFO::Fitting model to feature number 255, ASV_1692
#> 2023-05-08 20:11:40.621656 INFO::Fitting model to feature number 256, ASV_993
#> 2023-05-08 20:11:40.62529 INFO::Fitting model to feature number 257, ASV_1220
#> 2023-05-08 20:11:40.628321 INFO::Fitting model to feature number 258, ASV_1122
#> 2023-05-08 20:11:40.631248 INFO::Fitting model to feature number 259, ASV_2093
#> 2023-05-08 20:11:40.63455 INFO::Fitting model to feature number 260, ASV_2649
#> 2023-05-08 20:11:40.637598 INFO::Fitting model to feature number 261, ASV_2415
#> 2023-05-08 20:11:40.640974 INFO::Fitting model to feature number 262, ASV_778
#> 2023-05-08 20:11:40.644276 INFO::Fitting model to feature number 263, ASV_1180
#> 2023-05-08 20:11:40.64741 INFO::Fitting model to feature number 264, ASV_1086
#> 2023-05-08 20:11:40.650381 INFO::Fitting model to feature number 265, ASV_1132
#> 2023-05-08 20:11:40.653365 INFO::Fitting model to feature number 266, ASV_1806
#> 2023-05-08 20:11:40.656563 INFO::Fitting model to feature number 267, ASV_1372
#> 2023-05-08 20:11:40.659606 INFO::Fitting model to feature number 268, ASV_2839
#> 2023-05-08 20:11:40.662594 INFO::Fitting model to feature number 269, ASV_2038
#> 2023-05-08 20:11:40.665507 INFO::Fitting model to feature number 270, ASV_13
#> 2023-05-08 20:11:40.668414 INFO::Fitting model to feature number 271, ASV_231
#> 2023-05-08 20:11:40.671675 INFO::Fitting model to feature number 272, ASV_422
#> 2023-05-08 20:11:40.674737 INFO::Fitting model to feature number 273, ASV_1028
#> 2023-05-08 20:11:40.677852 INFO::Fitting model to feature number 274, ASV_1665
#> 2023-05-08 20:11:40.681103 INFO::Fitting model to feature number 275, ASV_2526
#> 2023-05-08 20:11:40.6844 INFO::Fitting model to feature number 276, ASV_2225
#> 2023-05-08 20:11:40.688067 INFO::Fitting model to feature number 277, ASV_2414
#> 2023-05-08 20:11:40.691347 INFO::Fitting model to feature number 278, ASV_47
#> 2023-05-08 20:11:40.694373 INFO::Fitting model to feature number 279, ASV_72
#> 2023-05-08 20:11:40.697607 INFO::Fitting model to feature number 280, ASV_75
#> 2023-05-08 20:11:40.700634 INFO::Fitting model to feature number 281, ASV_118
#> 2023-05-08 20:11:40.703813 INFO::Fitting model to feature number 282, ASV_782
#> 2023-05-08 20:11:40.707124 INFO::Fitting model to feature number 283, ASV_405
#> 2023-05-08 20:11:40.71023 INFO::Fitting model to feature number 284, ASV_715
#> 2023-05-08 20:11:40.713344 INFO::Fitting model to feature number 285, ASV_1271
#> 2023-05-08 20:11:40.716571 INFO::Fitting model to feature number 286, ASV_2514
#> 2023-05-08 20:11:40.719875 INFO::Fitting model to feature number 287, ASV_506
#> 2023-05-08 20:11:40.722977 INFO::Fitting model to feature number 288, ASV_1135
#> 2023-05-08 20:11:40.72592 INFO::Fitting model to feature number 289, ASV_1205
#> 2023-05-08 20:11:40.729402 INFO::Fitting model to feature number 290, ASV_1549
#> 2023-05-08 20:11:40.733818 INFO::Fitting model to feature number 291, ASV_104
#> 2023-05-08 20:11:40.737126 INFO::Fitting model to feature number 292, ASV_1081
#> 2023-05-08 20:11:40.740152 INFO::Fitting model to feature number 293, ASV_1404
#> 2023-05-08 20:11:40.743238 INFO::Fitting model to feature number 294, ASV_1750
#> 2023-05-08 20:11:40.746187 INFO::Fitting model to feature number 295, ASV_2215
#> 2023-05-08 20:11:40.749351 INFO::Fitting model to feature number 296, ASV_2026
#> 2023-05-08 20:11:40.752298 INFO::Fitting model to feature number 297, ASV_1936
#> 2023-05-08 20:11:40.755285 INFO::Fitting model to feature number 298, ASV_2394
#> 2023-05-08 20:11:40.758486 INFO::Fitting model to feature number 299, ASV_2631
#> 2023-05-08 20:11:40.761389 INFO::Fitting model to feature number 300, ASV_2391
#> 2023-05-08 20:11:40.764489 INFO::Fitting model to feature number 301, ASV_7
#> 2023-05-08 20:11:40.767422 INFO::Fitting model to feature number 302, ASV_931
#> 2023-05-08 20:11:40.770536 INFO::Fitting model to feature number 303, ASV_12
#> 2023-05-08 20:11:40.773644 INFO::Fitting model to feature number 304, ASV_408
#> 2023-05-08 20:11:40.776563 INFO::Fitting model to feature number 305, ASV_1794
#> 2023-05-08 20:11:40.779599 INFO::Fitting model to feature number 306, ASV_817
#> 2023-05-08 20:11:40.783388 INFO::Fitting model to feature number 307, ASV_1320
#> 2023-05-08 20:11:40.787519 INFO::Fitting model to feature number 308, ASV_2248
#> 2023-05-08 20:11:40.790894 INFO::Fitting model to feature number 309, ASV_1406
#> 2023-05-08 20:11:40.794073 INFO::Fitting model to feature number 310, ASV_2265
#> 2023-05-08 20:11:40.797149 INFO::Fitting model to feature number 311, ASV_51
#> 2023-05-08 20:11:40.800078 INFO::Fitting model to feature number 312, ASV_165
#> 2023-05-08 20:11:40.803029 INFO::Fitting model to feature number 313, ASV_258
#> 2023-05-08 20:11:40.806145 INFO::Fitting model to feature number 314, ASV_1007
#> 2023-05-08 20:11:40.809371 INFO::Fitting model to feature number 315, ASV_1217
#> 2023-05-08 20:11:40.812613 INFO::Fitting model to feature number 316, ASV_177
#> 2023-05-08 20:11:40.815693 INFO::Fitting model to feature number 317, ASV_889
#> 2023-05-08 20:11:40.818653 INFO::Fitting model to feature number 318, ASV_1582
#> 2023-05-08 20:11:40.821522 INFO::Fitting model to feature number 319, ASV_1018
#> 2023-05-08 20:11:40.82436 INFO::Fitting model to feature number 320, ASV_1906
#> 2023-05-08 20:11:40.827288 INFO::Fitting model to feature number 321, ASV_2571
#> 2023-05-08 20:11:40.830637 INFO::Fitting model to feature number 322, ASV_1931
#> 2023-05-08 20:11:40.833611 INFO::Fitting model to feature number 323, ASV_917
#> 2023-05-08 20:11:40.836522 INFO::Fitting model to feature number 324, ASV_1677
#> 2023-05-08 20:11:40.839444 INFO::Fitting model to feature number 325, ASV_1291
#> 2023-05-08 20:11:40.842384 INFO::Fitting model to feature number 326, ASV_2654
#> 2023-05-08 20:11:40.845561 INFO::Fitting model to feature number 327, ASV_1719
#> 2023-05-08 20:11:40.848521 INFO::Fitting model to feature number 328, ASV_1803
#> 2023-05-08 20:11:40.851388 INFO::Fitting model to feature number 329, ASV_2595
#> 2023-05-08 20:11:40.854283 INFO::Fitting model to feature number 330, ASV_2843
#> 2023-05-08 20:11:40.857148 INFO::Fitting model to feature number 331, ASV_9
#> 2023-05-08 20:11:40.860481 INFO::Fitting model to feature number 332, ASV_31
#> 2023-05-08 20:11:40.863559 INFO::Fitting model to feature number 333, ASV_86
#> 2023-05-08 20:11:40.866518 INFO::Fitting model to feature number 334, ASV_465
#> 2023-05-08 20:11:40.869471 INFO::Fitting model to feature number 335, ASV_753
#> 2023-05-08 20:11:40.872592 INFO::Fitting model to feature number 336, ASV_705
#> 2023-05-08 20:11:40.87627 INFO::Fitting model to feature number 337, ASV_706
#> 2023-05-08 20:11:40.879704 INFO::Fitting model to feature number 338, ASV_303
#> 2023-05-08 20:11:40.882706 INFO::Fitting model to feature number 339, ASV_634
#> 2023-05-08 20:11:40.885645 INFO::Fitting model to feature number 340, ASV_1861
#> 2023-05-08 20:11:40.888518 INFO::Fitting model to feature number 341, ASV_1924
#> 2023-05-08 20:11:40.891689 INFO::Fitting model to feature number 342, ASV_627
#> 2023-05-08 20:11:40.894716 INFO::Fitting model to feature number 343, ASV_1027
#> 2023-05-08 20:11:40.897687 INFO::Fitting model to feature number 344, ASV_1232
#> 2023-05-08 20:11:40.900616 INFO::Fitting model to feature number 345, ASV_1912
#> 2023-05-08 20:11:40.903525 INFO::Fitting model to feature number 346, ASV_811
#> 2023-05-08 20:11:40.906695 INFO::Fitting model to feature number 347, ASV_895
#> 2023-05-08 20:11:40.909685 INFO::Fitting model to feature number 348, ASV_955
#> 2023-05-08 20:11:40.912701 INFO::Fitting model to feature number 349, ASV_939
#> 2023-05-08 20:11:40.915726 INFO::Fitting model to feature number 350, ASV_158
#> 2023-05-08 20:11:40.918698 INFO::Fitting model to feature number 351, ASV_244
#> 2023-05-08 20:11:40.921832 INFO::Fitting model to feature number 352, ASV_559
#> 2023-05-08 20:11:40.925745 INFO::Fitting model to feature number 353, ASV_263
#> 2023-05-08 20:11:40.928879 INFO::Fitting model to feature number 354, ASV_1428
#> 2023-05-08 20:11:40.931868 INFO::Fitting model to feature number 355, ASV_2109
#> 2023-05-08 20:11:40.935089 INFO::Fitting model to feature number 356, ASV_215
#> 2023-05-08 20:11:40.938279 INFO::Fitting model to feature number 357, ASV_454
#> 2023-05-08 20:11:40.942635 INFO::Fitting model to feature number 358, ASV_1129
#> 2023-05-08 20:11:40.945737 INFO::Fitting model to feature number 359, ASV_1030
#> 2023-05-08 20:11:40.948938 INFO::Fitting model to feature number 360, ASV_1490
#> 2023-05-08 20:11:40.951848 INFO::Fitting model to feature number 361, ASV_1006
#> 2023-05-08 20:11:40.954994 INFO::Fitting model to feature number 362, ASV_1744
#> 2023-05-08 20:11:40.957931 INFO::Fitting model to feature number 363, ASV_1151
#> 2023-05-08 20:11:40.961133 INFO::Fitting model to feature number 364, ASV_513
#> 2023-05-08 20:11:40.964214 INFO::Fitting model to feature number 365, ASV_613
#> 2023-05-08 20:11:40.967558 INFO::Fitting model to feature number 366, ASV_2114
#> 2023-05-08 20:11:40.971132 INFO::Fitting model to feature number 367, ASV_2223
#> 2023-05-08 20:11:40.974534 INFO::Fitting model to feature number 368, ASV_2368
#> 2023-05-08 20:11:40.977602 INFO::Fitting model to feature number 369, ASV_2605
#> 2023-05-08 20:11:40.980674 INFO::Fitting model to feature number 370, ASV_2465
#> 2023-05-08 20:11:40.983677 INFO::Fitting model to feature number 371, ASV_2046
#> 2023-05-08 20:11:40.987067 INFO::Fitting model to feature number 372, ASV_235
#> 2023-05-08 20:11:40.990099 INFO::Fitting model to feature number 373, ASV_1650
#> 2023-05-08 20:11:40.993043 INFO::Fitting model to feature number 374, ASV_1727
#> 2023-05-08 20:11:40.995941 INFO::Fitting model to feature number 375, ASV_316
#> 2023-05-08 20:11:40.998784 INFO::Fitting model to feature number 376, ASV_1354
#> 2023-05-08 20:11:41.00173 INFO::Fitting model to feature number 377, ASV_894
#> 2023-05-08 20:11:41.004851 INFO::Fitting model to feature number 378, ASV_345
#> 2023-05-08 20:11:41.007811 INFO::Fitting model to feature number 379, ASV_372
#> 2023-05-08 20:11:41.010982 INFO::Fitting model to feature number 380, ASV_656
#> 2023-05-08 20:11:41.01395 INFO::Fitting model to feature number 381, ASV_1707
#> 2023-05-08 20:11:41.016831 INFO::Fitting model to feature number 382, ASV_2095
#> 2023-05-08 20:11:41.019722 INFO::Fitting model to feature number 383, ASV_117
#> 2023-05-08 20:11:41.022637 INFO::Fitting model to feature number 384, ASV_962
#> 2023-05-08 20:11:41.02568 INFO::Fitting model to feature number 385, ASV_621
#> 2023-05-08 20:11:41.028644 INFO::Fitting model to feature number 386, ASV_2362
#> 2023-05-08 20:11:41.031495 INFO::Fitting model to feature number 387, ASV_1401
#> 2023-05-08 20:11:41.03453 INFO::Fitting model to feature number 388, ASV_467
#> 2023-05-08 20:11:41.03772 INFO::Fitting model to feature number 389, ASV_1251
#> 2023-05-08 20:11:41.041249 INFO::Fitting model to feature number 390, ASV_739
#> 2023-05-08 20:11:41.044311 INFO::Fitting model to feature number 391, ASV_1817
#> 2023-05-08 20:11:41.047187 INFO::Fitting model to feature number 392, ASV_649
#> 2023-05-08 20:11:41.050873 INFO::Fitting model to feature number 393, ASV_226
#> 2023-05-08 20:11:41.054167 INFO::Fitting model to feature number 394, ASV_256
#> 2023-05-08 20:11:41.057515 INFO::Fitting model to feature number 395, ASV_521
#> 2023-05-08 20:11:41.060501 INFO::Fitting model to feature number 396, ASV_929
#> 2023-05-08 20:11:41.063474 INFO::Fitting model to feature number 397, ASV_2325
#> 2023-05-08 20:11:41.066582 INFO::Fitting model to feature number 398, ASV_651
#> 2023-05-08 20:11:41.069496 INFO::Fitting model to feature number 399, ASV_1388
#> 2023-05-08 20:11:41.072539 INFO::Fitting model to feature number 400, ASV_2039
#> 2023-05-08 20:11:41.076159 INFO::Fitting model to feature number 401, ASV_2713
#> 2023-05-08 20:11:41.079468 INFO::Fitting model to feature number 402, ASV_279
#> 2023-05-08 20:11:41.083296 INFO::Fitting model to feature number 403, ASV_1184
#> 2023-05-08 20:11:41.086562 INFO::Fitting model to feature number 404, ASV_1384
#> 2023-05-08 20:11:41.089599 INFO::Fitting model to feature number 405, ASV_1655
#> 2023-05-08 20:11:41.092603 INFO::Fitting model to feature number 406, ASV_987
#> 2023-05-08 20:11:41.095676 INFO::Fitting model to feature number 407, ASV_1654
#> 2023-05-08 20:11:41.09895 INFO::Fitting model to feature number 408, ASV_1611
#> 2023-05-08 20:11:41.102229 INFO::Fitting model to feature number 409, ASV_2200
#> 2023-05-08 20:11:41.105323 INFO::Fitting model to feature number 410, ASV_1852
#> 2023-05-08 20:11:41.108529 INFO::Fitting model to feature number 411, ASV_396
#> 2023-05-08 20:11:41.111591 INFO::Fitting model to feature number 412, ASV_1534
#> 2023-05-08 20:11:41.114935 INFO::Fitting model to feature number 413, ASV_1645
#> 2023-05-08 20:11:41.118008 INFO::Fitting model to feature number 414, ASV_763
#> 2023-05-08 20:11:41.121111 INFO::Fitting model to feature number 415, ASV_481
#> 2023-05-08 20:11:41.124186 INFO::Fitting model to feature number 416, ASV_490
#> 2023-05-08 20:11:41.127181 INFO::Fitting model to feature number 417, ASV_1156
#> 2023-05-08 20:11:41.130471 INFO::Fitting model to feature number 418, ASV_2195
#> 2023-05-08 20:11:41.133641 INFO::Fitting model to feature number 419, ASV_52
#> 2023-05-08 20:11:41.13669 INFO::Fitting model to feature number 420, ASV_588
#> 2023-05-08 20:11:41.139716 INFO::Fitting model to feature number 421, ASV_2808
#> 2023-05-08 20:11:41.142717 INFO::Fitting model to feature number 422, ASV_964
#> 2023-05-08 20:11:41.146169 INFO::Fitting model to feature number 423, ASV_378
#> 2023-05-08 20:11:41.150107 INFO::Fitting model to feature number 424, ASV_582
#> 2023-05-08 20:11:41.153341 INFO::Fitting model to feature number 425, ASV_1976
#> 2023-05-08 20:11:41.156405 INFO::Fitting model to feature number 426, ASV_442
#> 2023-05-08 20:11:41.159586 INFO::Fitting model to feature number 427, ASV_2275
#> 2023-05-08 20:11:41.162844 INFO::Fitting model to feature number 428, ASV_2069
#> 2023-05-08 20:11:41.165978 INFO::Fitting model to feature number 429, ASV_539
#> 2023-05-08 20:11:41.169098 INFO::Fitting model to feature number 430, ASV_1110
#> 2023-05-08 20:11:41.172192 INFO::Fitting model to feature number 431, ASV_1193
#> 2023-05-08 20:11:41.175669 INFO::Fitting model to feature number 432, ASV_846
#> 2023-05-08 20:11:41.179524 INFO::Fitting model to feature number 433, ASV_1069
#> 2023-05-08 20:11:41.183102 INFO::Fitting model to feature number 434, ASV_1606
#> 2023-05-08 20:11:41.186522 INFO::Fitting model to feature number 435, ASV_650
#> 2023-05-08 20:11:41.189737 INFO::Fitting model to feature number 436, ASV_1531
#> 2023-05-08 20:11:41.193269 INFO::Fitting model to feature number 437, ASV_1576
#> 2023-05-08 20:11:41.196405 INFO::Fitting model to feature number 438, ASV_491
#> 2023-05-08 20:11:41.199454 INFO::Fitting model to feature number 439, ASV_746
#> 2023-05-08 20:11:41.202396 INFO::Fitting model to feature number 440, ASV_2826
#> 2023-05-08 20:11:41.205499 INFO::Fitting model to feature number 441, ASV_2017
#> 2023-05-08 20:11:41.20933 INFO::Fitting model to feature number 442, ASV_628
#> 2023-05-08 20:11:41.212755 INFO::Fitting model to feature number 443, ASV_2211
#> 2023-05-08 20:11:41.216073 INFO::Fitting model to feature number 444, ASV_1212
#> 2023-05-08 20:11:41.219115 INFO::Fitting model to feature number 445, ASV_550
#> 2023-05-08 20:11:41.222063 INFO::Fitting model to feature number 446, ASV_1450
#> 2023-05-08 20:11:41.225411 INFO::Fitting model to feature number 447, ASV_896
#> 2023-05-08 20:11:41.228478 INFO::Fitting model to feature number 448, ASV_979
#> 2023-05-08 20:11:41.231423 INFO::Fitting model to feature number 449, ASV_1421
#> 2023-05-08 20:11:41.234323 INFO::Fitting model to feature number 450, ASV_2421
#> 2023-05-08 20:11:41.237248 INFO::Fitting model to feature number 451, ASV_1500
#> 2023-05-08 20:11:41.240513 INFO::Fitting model to feature number 452, ASV_751
#> 2023-05-08 20:11:41.243617 INFO::Fitting model to feature number 453, ASV_2828
#> 2023-05-08 20:11:41.246997 INFO::Fitting model to feature number 454, ASV_2500
#> 2023-05-08 20:11:41.25022 INFO::Fitting model to feature number 455, ASV_2772
#> 2023-05-08 20:11:41.253209 INFO::Fitting model to feature number 456, ASV_320
#> 2023-05-08 20:11:41.256524 INFO::Fitting model to feature number 457, ASV_965
#> 2023-05-08 20:11:41.259768 INFO::Fitting model to feature number 458, ASV_18
#> 2023-05-08 20:11:41.262952 INFO::Fitting model to feature number 459, ASV_19
#> 2023-05-08 20:11:41.266172 INFO::Fitting model to feature number 460, ASV_1065
#> 2023-05-08 20:11:41.269049 INFO::Fitting model to feature number 461, ASV_1844
#> 2023-05-08 20:11:41.27206 INFO::Fitting model to feature number 462, ASV_42
#> 2023-05-08 20:11:41.275252 INFO::Fitting model to feature number 463, ASV_243
#> 2023-05-08 20:11:41.278292 INFO::Fitting model to feature number 464, ASV_517
#> 2023-05-08 20:11:41.281196 INFO::Fitting model to feature number 465, ASV_756
#> 2023-05-08 20:11:41.284116 INFO::Fitting model to feature number 466, ASV_45
#> 2023-05-08 20:11:41.287482 INFO::Fitting model to feature number 467, ASV_49
#> 2023-05-08 20:11:41.291073 INFO::Fitting model to feature number 468, ASV_154
#> 2023-05-08 20:11:41.294182 INFO::Fitting model to feature number 469, ASV_551
#> 2023-05-08 20:11:41.297204 INFO::Fitting model to feature number 470, ASV_224
#> 2023-05-08 20:11:41.300363 INFO::Fitting model to feature number 471, ASV_261
#> 2023-05-08 20:11:41.3041 INFO::Fitting model to feature number 472, ASV_1034
#> 2023-05-08 20:11:41.307371 INFO::Fitting model to feature number 473, ASV_523
#> 2023-05-08 20:11:41.310446 INFO::Fitting model to feature number 474, ASV_926
#> 2023-05-08 20:11:41.313399 INFO::Fitting model to feature number 475, ASV_1185
#> 2023-05-08 20:11:41.316496 INFO::Fitting model to feature number 476, ASV_274
#> 2023-05-08 20:11:41.319702 INFO::Fitting model to feature number 477, ASV_1489
#> 2023-05-08 20:11:41.322901 INFO::Fitting model to feature number 478, ASV_2030
#> 2023-05-08 20:11:41.326066 INFO::Fitting model to feature number 479, ASV_1659
#> 2023-05-08 20:11:41.32907 INFO::Fitting model to feature number 480, ASV_2420
#> 2023-05-08 20:11:41.33203 INFO::Fitting model to feature number 481, ASV_768
#> 2023-05-08 20:11:41.335609 INFO::Fitting model to feature number 482, ASV_209
#> 2023-05-08 20:11:41.338948 INFO::Fitting model to feature number 483, ASV_2258
#> 2023-05-08 20:11:41.342056 INFO::Fitting model to feature number 484, ASV_1218
#> 2023-05-08 20:11:41.345055 INFO::Fitting model to feature number 485, ASV_59
#> 2023-05-08 20:11:41.348024 INFO::Fitting model to feature number 486, ASV_87
#> 2023-05-08 20:11:41.351055 INFO::Fitting model to feature number 487, ASV_607
#> 2023-05-08 20:11:41.354099 INFO::Fitting model to feature number 488, ASV_1824
#> 2023-05-08 20:11:41.357633 INFO::Fitting model to feature number 489, ASV_2049
#> 2023-05-08 20:11:41.361089 INFO::Fitting model to feature number 490, ASV_840
#> 2023-05-08 20:11:41.364617 INFO::Fitting model to feature number 491, ASV_321
#> 2023-05-08 20:11:41.367794 INFO::Fitting model to feature number 492, ASV_2411
#> 2023-05-08 20:11:41.370985 INFO::Fitting model to feature number 493, ASV_815
#> 2023-05-08 20:11:41.374028 INFO::Fitting model to feature number 494, ASV_1455
#> 2023-05-08 20:11:41.376972 INFO::Fitting model to feature number 495, ASV_2899
#> 2023-05-08 20:11:41.380017 INFO::Fitting model to feature number 496, ASV_1272
#> 2023-05-08 20:11:41.383379 INFO::Fitting model to feature number 497, ASV_2324
#> 2023-05-08 20:11:41.386734 INFO::Fitting model to feature number 498, ASV_1841
#> 2023-05-08 20:11:41.389982 INFO::Fitting model to feature number 499, ASV_1967
#> 2023-05-08 20:11:41.393004 INFO::Fitting model to feature number 500, ASV_1578
#> 2023-05-08 20:11:41.395959 INFO::Fitting model to feature number 501, ASV_2866
#> 2023-05-08 20:11:41.398961 INFO::Fitting model to feature number 502, ASV_155
#> 2023-05-08 20:11:41.40207 INFO::Fitting model to feature number 503, ASV_284
#> 2023-05-08 20:11:41.405095 INFO::Fitting model to feature number 504, ASV_457
#> 2023-05-08 20:11:41.408213 INFO::Fitting model to feature number 505, ASV_1020
#> 2023-05-08 20:11:41.411397 INFO::Fitting model to feature number 506, ASV_1562
#> 2023-05-08 20:11:41.415107 INFO::Fitting model to feature number 507, ASV_1772
#> 2023-05-08 20:11:41.418411 INFO::Fitting model to feature number 508, ASV_2205
#> 2023-05-08 20:11:41.421552 INFO::Fitting model to feature number 509, ASV_2323
#> 2023-05-08 20:11:41.424555 INFO::Fitting model to feature number 510, ASV_925
#> 2023-05-08 20:11:41.427564 INFO::Fitting model to feature number 511, ASV_120
#> 2023-05-08 20:11:41.430691 INFO::Fitting model to feature number 512, ASV_1060
#> 2023-05-08 20:11:41.434478 INFO::Fitting model to feature number 513, ASV_1718
#> 2023-05-08 20:11:41.437965 INFO::Fitting model to feature number 514, ASV_307
#> 2023-05-08 20:11:41.441465 INFO::Fitting model to feature number 515, ASV_277
#> 2023-05-08 20:11:41.444579 INFO::Fitting model to feature number 516, ASV_852
#> 2023-05-08 20:11:41.447938 INFO::Fitting model to feature number 517, ASV_2154
#> 2023-05-08 20:11:41.451209 INFO::Fitting model to feature number 518, ASV_495
#> 2023-05-08 20:11:41.454296 INFO::Fitting model to feature number 519, ASV_784
#> 2023-05-08 20:11:41.458269 INFO::Fitting model to feature number 520, ASV_682
#> 2023-05-08 20:11:41.462016 INFO::Fitting model to feature number 521, ASV_961
#> 2023-05-08 20:11:41.465409 INFO::Fitting model to feature number 522, ASV_440
#> 2023-05-08 20:11:41.468605 INFO::Fitting model to feature number 523, ASV_675
#> 2023-05-08 20:11:41.471671 INFO::Fitting model to feature number 524, ASV_930
#> 2023-05-08 20:11:41.475033 INFO::Fitting model to feature number 525, ASV_20
#> 2023-05-08 20:11:41.478492 INFO::Fitting model to feature number 526, ASV_23
#> 2023-05-08 20:11:41.481738 INFO::Fitting model to feature number 527, ASV_149
#> 2023-05-08 20:11:41.484799 INFO::Fitting model to feature number 528, ASV_69
#> 2023-05-08 20:11:41.4878 INFO::Fitting model to feature number 529, ASV_968
#> 2023-05-08 20:11:41.491017 INFO::Fitting model to feature number 530, ASV_116
#> 2023-05-08 20:11:41.494516 INFO::Fitting model to feature number 531, ASV_109
#> 2023-05-08 20:11:41.497696 INFO::Fitting model to feature number 532, ASV_416
#> 2023-05-08 20:11:41.500767 INFO::Fitting model to feature number 533, ASV_1064
#> 2023-05-08 20:11:41.504083 INFO::Fitting model to feature number 534, ASV_1357
#> 2023-05-08 20:11:41.507443 INFO::Fitting model to feature number 535, ASV_610
#> 2023-05-08 20:11:41.51071 INFO::Fitting model to feature number 536, ASV_2151
#> 2023-05-08 20:11:41.51379 INFO::Fitting model to feature number 537, ASV_1101
#> 2023-05-08 20:11:41.516933 INFO::Fitting model to feature number 538, ASV_1121
#> 2023-05-08 20:11:41.520019 INFO::Fitting model to feature number 539, ASV_25
#> 2023-05-08 20:11:41.523248 INFO::Fitting model to feature number 540, ASV_26
#> 2023-05-08 20:11:41.526783 INFO::Fitting model to feature number 541, ASV_92
#> 2023-05-08 20:11:41.53008 INFO::Fitting model to feature number 542, ASV_1603
#> 2023-05-08 20:11:41.533359 INFO::Fitting model to feature number 543, ASV_289
#> 2023-05-08 20:11:41.536487 INFO::Fitting model to feature number 544, ASV_1188
#> 2023-05-08 20:11:41.539524 INFO::Fitting model to feature number 545, ASV_2600
#> 2023-05-08 20:11:41.543181 INFO::Fitting model to feature number 546, ASV_2739
#> 2023-05-08 20:11:41.546358 INFO::Fitting model to feature number 547, ASV_1624
#> 2023-05-08 20:11:41.549383 INFO::Fitting model to feature number 548, ASV_1799
#> 2023-05-08 20:11:41.552368 INFO::Fitting model to feature number 549, ASV_502
#> 2023-05-08 20:11:41.555558 INFO::Fitting model to feature number 550, ASV_2685
#> 2023-05-08 20:11:41.559458 INFO::Fitting model to feature number 551, ASV_1619
#> 2023-05-08 20:11:41.562961 INFO::Fitting model to feature number 552, ASV_2090
#> 2023-05-08 20:11:41.566159 INFO::Fitting model to feature number 553, ASV_1860
#> 2023-05-08 20:11:41.569967 INFO::Fitting model to feature number 554, ASV_2455
#> 2023-05-08 20:11:41.573238 INFO::Fitting model to feature number 555, ASV_2308
#> 2023-05-08 20:11:41.576505 INFO::Fitting model to feature number 556, ASV_1026
#> 2023-05-08 20:11:41.579627 INFO::Fitting model to feature number 557, ASV_193
#> 2023-05-08 20:11:41.582737 INFO::Fitting model to feature number 558, ASV_388
#> 2023-05-08 20:11:41.58577 INFO::Fitting model to feature number 559, ASV_707
#> 2023-05-08 20:11:41.589013 INFO::Fitting model to feature number 560, ASV_441
#> 2023-05-08 20:11:41.592034 INFO::Fitting model to feature number 561, ASV_672
#> 2023-05-08 20:11:41.594998 INFO::Fitting model to feature number 562, ASV_2426
#> 2023-05-08 20:11:41.59822 INFO::Fitting model to feature number 563, ASV_913
#> 2023-05-08 20:11:41.601513 INFO::Fitting model to feature number 564, ASV_2546
#> 2023-05-08 20:11:41.605041 INFO::Fitting model to feature number 565, ASV_394
#> 2023-05-08 20:11:41.608825 INFO::Fitting model to feature number 566, ASV_1785
#> 2023-05-08 20:11:41.612077 INFO::Fitting model to feature number 567, ASV_620
#> 2023-05-08 20:11:41.615183 INFO::Fitting model to feature number 568, ASV_1389
#> 2023-05-08 20:11:41.618597 INFO::Fitting model to feature number 569, ASV_2565
#> 2023-05-08 20:11:41.622052 INFO::Fitting model to feature number 570, ASV_864
#> 2023-05-08 20:11:41.625301 INFO::Fitting model to feature number 571, ASV_1721
#> 2023-05-08 20:11:41.628382 INFO::Fitting model to feature number 572, ASV_273
#> 2023-05-08 20:11:41.631634 INFO::Fitting model to feature number 573, ASV_752
#> 2023-05-08 20:11:41.634886 INFO::Fitting model to feature number 574, ASV_407
#> 2023-05-08 20:11:41.637924 INFO::Fitting model to feature number 575, ASV_1301
#> 2023-05-08 20:11:41.640899 INFO::Fitting model to feature number 576, ASV_1543
#> 2023-05-08 20:11:41.643944 INFO::Fitting model to feature number 577, ASV_2429
#> 2023-05-08 20:11:41.64698 INFO::Fitting model to feature number 578, ASV_1552
#> 2023-05-08 20:11:41.650339 INFO::Fitting model to feature number 579, ASV_2050
#> 2023-05-08 20:11:41.653709 INFO::Fitting model to feature number 580, ASV_2905
#> 2023-05-08 20:11:41.656856 INFO::Fitting model to feature number 581, ASV_812
#> 2023-05-08 20:11:41.659858 INFO::Fitting model to feature number 582, ASV_836
#> 2023-05-08 20:11:41.662837 INFO::Fitting model to feature number 583, ASV_873
#> 2023-05-08 20:11:41.665849 INFO::Fitting model to feature number 584, ASV_1477
#> 2023-05-08 20:11:41.669015 INFO::Fitting model to feature number 585, ASV_2439
#> 2023-05-08 20:11:41.672288 INFO::Fitting model to feature number 586, ASV_466
#> 2023-05-08 20:11:41.675405 INFO::Fitting model to feature number 587, ASV_2194
#> 2023-05-08 20:11:41.678377 INFO::Fitting model to feature number 588, ASV_698
#> 2023-05-08 20:11:41.681545 INFO::Fitting model to feature number 589, ASV_1025
#> 2023-05-08 20:11:41.68502 INFO::Fitting model to feature number 590, ASV_1527
#> 2023-05-08 20:11:41.688383 INFO::Fitting model to feature number 591, ASV_586
#> 2023-05-08 20:11:41.691867 INFO::Fitting model to feature number 592, ASV_21
#> 2023-05-08 20:11:41.695073 INFO::Fitting model to feature number 593, ASV_804
#> 2023-05-08 20:11:41.698628 INFO::Fitting model to feature number 594, ASV_2440
#> 2023-05-08 20:11:41.701944 INFO::Fitting model to feature number 595, ASV_832
#> 2023-05-08 20:11:41.705058 INFO::Fitting model to feature number 596, ASV_909
#> 2023-05-08 20:11:41.708208 INFO::Fitting model to feature number 597, ASV_1281
#> 2023-05-08 20:11:41.711645 INFO::Fitting model to feature number 598, ASV_1236
#> 2023-05-08 20:11:41.715145 INFO::Fitting model to feature number 599, ASV_1951
#> 2023-05-08 20:11:41.71841 INFO::Fitting model to feature number 600, ASV_400
#> 2023-05-08 20:11:41.721601 INFO::Fitting model to feature number 601, ASV_1843
#> 2023-05-08 20:11:41.724848 INFO::Fitting model to feature number 602, ASV_1514
#> 2023-05-08 20:11:41.727876 INFO::Fitting model to feature number 603, ASV_2747
#> 2023-05-08 20:11:41.731503 INFO::Fitting model to feature number 604, ASV_1544
#> 2023-05-08 20:11:41.73459 INFO::Fitting model to feature number 605, ASV_742
#> 2023-05-08 20:11:41.737868 INFO::Fitting model to feature number 606, ASV_1975
#> 2023-05-08 20:11:41.741229 INFO::Fitting model to feature number 607, ASV_1797
#> 2023-05-08 20:11:41.744404 INFO::Fitting model to feature number 608, ASV_2554
#> 2023-05-08 20:11:41.74797 INFO::Fitting model to feature number 609, ASV_1884
#> 2023-05-08 20:11:41.751115 INFO::Fitting model to feature number 610, ASV_869
#> 2023-05-08 20:11:41.754176 INFO::Fitting model to feature number 611, ASV_1297
#> 2023-05-08 20:11:41.75724 INFO::Fitting model to feature number 612, ASV_2548
#> 2023-05-08 20:11:41.760328 INFO::Fitting model to feature number 613, ASV_1664
#> 2023-05-08 20:11:41.763981 INFO::Fitting model to feature number 614, ASV_1684
#> 2023-05-08 20:11:41.76737 INFO::Fitting model to feature number 615, ASV_2523
#> 2023-05-08 20:11:41.770545 INFO::Fitting model to feature number 616, ASV_2290
#> 2023-05-08 20:11:41.773594 INFO::Fitting model to feature number 617, ASV_2585
#> 2023-05-08 20:11:41.777421 INFO::Fitting model to feature number 618, ASV_622
#> 2023-05-08 20:11:41.780583 INFO::Fitting model to feature number 619, ASV_1548
#> 2023-05-08 20:11:41.783645 INFO::Fitting model to feature number 620, ASV_1957
#> 2023-05-08 20:11:41.786825 INFO::Fitting model to feature number 621, ASV_2553
#> 2023-05-08 20:11:41.790861 INFO::Fitting model to feature number 622, ASV_2570
#> 2023-05-08 20:11:41.794205 INFO::Fitting model to feature number 623, ASV_2656
#> 2023-05-08 20:11:41.797543 INFO::Fitting model to feature number 624, ASV_806
#> 2023-05-08 20:11:41.800876 INFO::Fitting model to feature number 625, ASV_2182
#> 2023-05-08 20:11:41.804039 INFO::Fitting model to feature number 626, ASV_1754
#> 2023-05-08 20:11:41.807422 INFO::Fitting model to feature number 627, ASV_126
#> 2023-05-08 20:11:41.811021 INFO::Fitting model to feature number 628, ASV_353
#> 2023-05-08 20:11:41.814524 INFO::Fitting model to feature number 629, ASV_137
#> 2023-05-08 20:11:41.817717 INFO::Fitting model to feature number 630, ASV_366
#> 2023-05-08 20:11:41.820941 INFO::Fitting model to feature number 631, ASV_1344
#> 2023-05-08 20:11:41.825097 INFO::Fitting model to feature number 632, ASV_1084
#> 2023-05-08 20:11:41.828421 INFO::Fitting model to feature number 633, ASV_240
#> 2023-05-08 20:11:41.831632 INFO::Fitting model to feature number 634, ASV_1857
#> 2023-05-08 20:11:41.834749 INFO::Fitting model to feature number 635, ASV_727
#> 2023-05-08 20:11:41.838155 INFO::Fitting model to feature number 636, ASV_1400
#> 2023-05-08 20:11:41.841815 INFO::Fitting model to feature number 637, ASV_847
#> 2023-05-08 20:11:41.845002 INFO::Fitting model to feature number 638, ASV_1368
#> 2023-05-08 20:11:41.848047 INFO::Fitting model to feature number 639, ASV_1561
#> 2023-05-08 20:11:41.851065 INFO::Fitting model to feature number 640, ASV_1938
#> 2023-05-08 20:11:41.854403 INFO::Fitting model to feature number 641, ASV_631
#> 2023-05-08 20:11:41.857807 INFO::Fitting model to feature number 642, ASV_1622
#> 2023-05-08 20:11:41.861011 INFO::Fitting model to feature number 643, ASV_700
#> 2023-05-08 20:11:41.864199 INFO::Fitting model to feature number 644, ASV_985
#> 2023-05-08 20:11:41.867387 INFO::Fitting model to feature number 645, ASV_822
#> 2023-05-08 20:11:41.870765 INFO::Fitting model to feature number 646, ASV_799
#> 2023-05-08 20:11:41.873978 INFO::Fitting model to feature number 647, ASV_1891
#> 2023-05-08 20:11:41.877253 INFO::Fitting model to feature number 648, ASV_10
#> 2023-05-08 20:11:41.880291 INFO::Fitting model to feature number 649, ASV_1676
#> 2023-05-08 20:11:41.883541 INFO::Fitting model to feature number 650, ASV_48
#> 2023-05-08 20:11:41.886832 INFO::Fitting model to feature number 651, ASV_108
#> 2023-05-08 20:11:41.889913 INFO::Fitting model to feature number 652, ASV_2155
#> 2023-05-08 20:11:41.892947 INFO::Fitting model to feature number 653, ASV_2580
#> 2023-05-08 20:11:41.896003 INFO::Fitting model to feature number 654, ASV_597
#> 2023-05-08 20:11:41.899466 INFO::Fitting model to feature number 655, ASV_796
#> 2023-05-08 20:11:41.903248 INFO::Fitting model to feature number 656, ASV_1899
#> 2023-05-08 20:11:41.906782 INFO::Fitting model to feature number 657, ASV_2024
#> 2023-05-08 20:11:41.910208 INFO::Fitting model to feature number 658, ASV_2816
#> 2023-05-08 20:11:41.913517 INFO::Fitting model to feature number 659, ASV_1743
#> 2023-05-08 20:11:41.916974 INFO::Fitting model to feature number 660, ASV_62
#> 2023-05-08 20:11:41.920065 INFO::Fitting model to feature number 661, ASV_554
#> 2023-05-08 20:11:41.923059 INFO::Fitting model to feature number 662, ASV_1442
#> 2023-05-08 20:11:41.926106 INFO::Fitting model to feature number 663, ASV_1738
#> 2023-05-08 20:11:41.929221 INFO::Fitting model to feature number 664, ASV_1483
#> 2023-05-08 20:11:41.932523 INFO::Fitting model to feature number 665, ASV_296
#> 2023-05-08 20:11:41.935663 INFO::Fitting model to feature number 666, ASV_572
#> 2023-05-08 20:11:41.938706 INFO::Fitting model to feature number 667, ASV_1834
#> 2023-05-08 20:11:41.94188 INFO::Fitting model to feature number 668, ASV_693
#> 2023-05-08 20:11:41.945126 INFO::Fitting model to feature number 669, ASV_2567
#> 2023-05-08 20:11:41.948913 INFO::Fitting model to feature number 670, ASV_15
#> 2023-05-08 20:11:41.952116 INFO::Fitting model to feature number 671, ASV_27
#> 2023-05-08 20:11:41.955207 INFO::Fitting model to feature number 672, ASV_74
#> 2023-05-08 20:11:41.958246 INFO::Fitting model to feature number 673, ASV_188
#> 2023-05-08 20:11:41.961213 INFO::Fitting model to feature number 674, ASV_2622
#> 2023-05-08 20:11:41.964521 INFO::Fitting model to feature number 675, ASV_731
#> 2023-05-08 20:11:41.96759 INFO::Fitting model to feature number 676, ASV_2243
#> 2023-05-08 20:11:41.970822 INFO::Fitting model to feature number 677, ASV_197
#> 2023-05-08 20:11:41.973999 INFO::Fitting model to feature number 678, ASV_590
#> 2023-05-08 20:11:41.977773 INFO::Fitting model to feature number 679, ASV_708
#> 2023-05-08 20:11:41.981088 INFO::Fitting model to feature number 680, ASV_1984
#> 2023-05-08 20:11:41.984199 INFO::Fitting model to feature number 681, ASV_2111
#> 2023-05-08 20:11:41.987318 INFO::Fitting model to feature number 682, ASV_486
#> 2023-05-08 20:11:41.990564 INFO::Fitting model to feature number 683, ASV_248
#> 2023-05-08 20:11:41.994223 INFO::Fitting model to feature number 684, ASV_1635
#> 2023-05-08 20:11:41.997401 INFO::Fitting model to feature number 685, ASV_747
#> 2023-05-08 20:11:42.000442 INFO::Fitting model to feature number 686, ASV_2383
#> 2023-05-08 20:11:42.003433 INFO::Fitting model to feature number 687, ASV_1073
#> 2023-05-08 20:11:42.00636 INFO::Fitting model to feature number 688, ASV_1869
#> 2023-05-08 20:11:42.009841 INFO::Fitting model to feature number 689, ASV_2787
#> 2023-05-08 20:11:42.01306 INFO::Fitting model to feature number 690, ASV_1155
#> 2023-05-08 20:11:42.016139 INFO::Fitting model to feature number 691, ASV_1793
#> 2023-05-08 20:11:42.019097 INFO::Fitting model to feature number 692, ASV_1565
#> 2023-05-08 20:11:42.022046 INFO::Fitting model to feature number 693, ASV_2627
#> 2023-05-08 20:11:42.025422 INFO::Fitting model to feature number 694, ASV_2657
#> 2023-05-08 20:11:42.028838 INFO::Fitting model to feature number 695, ASV_2064
#> 2023-05-08 20:11:42.032087 INFO::Fitting model to feature number 696, ASV_921
#> 2023-05-08 20:11:42.035229 INFO::Fitting model to feature number 697, ASV_2876
#> 2023-05-08 20:11:42.038394 INFO::Fitting model to feature number 698, ASV_2763
#> 2023-05-08 20:11:42.041759 INFO::Fitting model to feature number 699, ASV_103
#> 2023-05-08 20:11:42.045115 INFO::Fitting model to feature number 700, ASV_298
#> 2023-05-08 20:11:42.048311 INFO::Fitting model to feature number 701, ASV_1237
#> 2023-05-08 20:11:42.051769 INFO::Fitting model to feature number 702, ASV_1815
#> 2023-05-08 20:11:42.055046 INFO::Fitting model to feature number 703, ASV_2635
#> 2023-05-08 20:11:42.058918 INFO::Fitting model to feature number 704, ASV_134
#> 2023-05-08 20:11:42.062196 INFO::Fitting model to feature number 705, ASV_874
#> 2023-05-08 20:11:42.065371 INFO::Fitting model to feature number 706, ASV_948
#> 2023-05-08 20:11:42.068416 INFO::Fitting model to feature number 707, ASV_1569
#> 2023-05-08 20:11:42.071421 INFO::Fitting model to feature number 708, ASV_2244
#> 2023-05-08 20:11:42.074503 INFO::Fitting model to feature number 709, ASV_2224
#> 2023-05-08 20:11:42.077618 INFO::Fitting model to feature number 710, ASV_389
#> 2023-05-08 20:11:42.080718 INFO::Fitting model to feature number 711, ASV_645
#> 2023-05-08 20:11:42.08385 INFO::Fitting model to feature number 712, ASV_888
#> 2023-05-08 20:11:42.08687 INFO::Fitting model to feature number 713, ASV_2527
#> 2023-05-08 20:11:42.091142 INFO::Fitting model to feature number 714, ASV_413
#> 2023-05-08 20:11:42.094628 INFO::Fitting model to feature number 715, ASV_1872
#> 2023-05-08 20:11:42.097914 INFO::Fitting model to feature number 716, ASV_826
#> 2023-05-08 20:11:42.101079 INFO::Fitting model to feature number 717, ASV_1765
#> 2023-05-08 20:11:42.104551 INFO::Fitting model to feature number 718, ASV_1708
#> 2023-05-08 20:11:42.107855 INFO::Fitting model to feature number 719, ASV_2785
#> 2023-05-08 20:11:42.110986 INFO::Fitting model to feature number 720, ASV_380
#> 2023-05-08 20:11:42.114344 INFO::Fitting model to feature number 721, ASV_1711
#> 2023-05-08 20:11:42.117417 INFO::Fitting model to feature number 722, ASV_412
#> 2023-05-08 20:11:42.120531 INFO::Fitting model to feature number 723, ASV_1971
#> 2023-05-08 20:11:42.123536 INFO::Fitting model to feature number 724, ASV_2564
#> 2023-05-08 20:11:42.126716 INFO::Fitting model to feature number 725, ASV_1651
#> 2023-05-08 20:11:42.130166 INFO::Fitting model to feature number 726, ASV_2073
#> 2023-05-08 20:11:42.133404 INFO::Fitting model to feature number 727, ASV_1070
#> 2023-05-08 20:11:42.136677 INFO::Fitting model to feature number 728, ASV_726
#> 2023-05-08 20:11:42.140052 INFO::Fitting model to feature number 729, ASV_1890
#> 2023-05-08 20:11:42.143378 INFO::Fitting model to feature number 730, ASV_689
#> 2023-05-08 20:11:42.146591 INFO::Fitting model to feature number 731, ASV_2160
#> 2023-05-08 20:11:42.149893 INFO::Fitting model to feature number 732, ASV_1734
#> 2023-05-08 20:11:42.153452 INFO::Fitting model to feature number 733, ASV_2065
#> 2023-05-08 20:11:42.157019 INFO::Fitting model to feature number 734, ASV_280
#> 2023-05-08 20:11:42.160352 INFO::Fitting model to feature number 735, ASV_430
#> 2023-05-08 20:11:42.163534 INFO::Fitting model to feature number 736, ASV_290
#> 2023-05-08 20:11:42.166894 INFO::Fitting model to feature number 737, ASV_736
#> 2023-05-08 20:11:42.169931 INFO::Fitting model to feature number 738, ASV_1395
#> 2023-05-08 20:11:42.172941 INFO::Fitting model to feature number 739, ASV_1147
#> 2023-05-08 20:11:42.176049 INFO::Fitting model to feature number 740, ASV_2467
#> 2023-05-08 20:11:42.179228 INFO::Fitting model to feature number 741, ASV_2873
#> 2023-05-08 20:11:42.182549 INFO::Fitting model to feature number 742, ASV_22
#> 2023-05-08 20:11:42.186039 INFO::Fitting model to feature number 743, ASV_625
#> 2023-05-08 20:11:42.189238 INFO::Fitting model to feature number 744, ASV_996
#> 2023-05-08 20:11:42.19226 INFO::Fitting model to feature number 745, ASV_232
#> 2023-05-08 20:11:42.195313 INFO::Fitting model to feature number 746, ASV_606
#> 2023-05-08 20:11:42.198931 INFO::Fitting model to feature number 747, ASV_770
#> 2023-05-08 20:11:42.202404 INFO::Fitting model to feature number 748, ASV_2547
#> 2023-05-08 20:11:42.205647 INFO::Fitting model to feature number 749, ASV_148
#> 2023-05-08 20:11:42.208755 INFO::Fitting model to feature number 750, ASV_211
#> 2023-05-08 20:11:42.211911 INFO::Fitting model to feature number 751, ASV_461
#> 2023-05-08 20:11:42.215235 INFO::Fitting model to feature number 752, ASV_624
#> 2023-05-08 20:11:42.218445 INFO::Fitting model to feature number 753, ASV_1349
#> 2023-05-08 20:11:42.221483 INFO::Fitting model to feature number 754, ASV_713
#> 2023-05-08 20:11:42.224586 INFO::Fitting model to feature number 755, ASV_1158
#> 2023-05-08 20:11:42.22762 INFO::Fitting model to feature number 756, ASV_2285
#> 2023-05-08 20:11:42.23121 INFO::Fitting model to feature number 757, ASV_678
#> 2023-05-08 20:11:42.23441 INFO::Fitting model to feature number 758, ASV_1875
#> 2023-05-08 20:11:42.237741 INFO::Fitting model to feature number 759, ASV_1537
#> 2023-05-08 20:11:42.240898 INFO::Fitting model to feature number 760, ASV_2145
#> 2023-05-08 20:11:42.243948 INFO::Fitting model to feature number 761, ASV_1259
#> 2023-05-08 20:11:42.247228 INFO::Fitting model to feature number 762, ASV_1414
#> 2023-05-08 20:11:42.25042 INFO::Fitting model to feature number 763, ASV_1699
#> 2023-05-08 20:11:42.253492 INFO::Fitting model to feature number 764, ASV_779
#> 2023-05-08 20:11:42.256542 INFO::Fitting model to feature number 765, ASV_1100
#> 2023-05-08 20:11:42.259697 INFO::Fitting model to feature number 766, ASV_1033
#> 2023-05-08 20:11:42.262717 INFO::Fitting model to feature number 767, ASV_1411
#> 2023-05-08 20:11:42.265684 INFO::Fitting model to feature number 768, ASV_1868
#> 2023-05-08 20:11:42.268616 INFO::Fitting model to feature number 769, ASV_1805
#> 2023-05-08 20:11:42.271648 INFO::Fitting model to feature number 770, ASV_1077
#> 2023-05-08 20:11:42.274868 INFO::Fitting model to feature number 771, ASV_2677
#> 2023-05-08 20:11:42.278521 INFO::Fitting model to feature number 772, ASV_2279
#> 2023-05-08 20:11:42.281885 INFO::Fitting model to feature number 773, ASV_2855
#> 2023-05-08 20:11:42.285122 INFO::Fitting model to feature number 774, ASV_157
#> 2023-05-08 20:11:42.288325 INFO::Fitting model to feature number 775, ASV_190
#> 2023-05-08 20:11:42.291671 INFO::Fitting model to feature number 776, ASV_655
#> 2023-05-08 20:11:42.295082 INFO::Fitting model to feature number 777, ASV_947
#> 2023-05-08 20:11:42.298192 INFO::Fitting model to feature number 778, ASV_592
#> 2023-05-08 20:11:42.30146 INFO::Fitting model to feature number 779, ASV_1954
#> 2023-05-08 20:11:42.304641 INFO::Fitting model to feature number 780, ASV_206
#> 2023-05-08 20:11:42.308048 INFO::Fitting model to feature number 781, ASV_357
#> 2023-05-08 20:11:42.31143 INFO::Fitting model to feature number 782, ASV_238
#> 2023-05-08 20:11:42.314684 INFO::Fitting model to feature number 783, ASV_512
#> 2023-05-08 20:11:42.317848 INFO::Fitting model to feature number 784, ASV_2693
#> 2023-05-08 20:11:42.320812 INFO::Fitting model to feature number 785, ASV_2791
#> 2023-05-08 20:11:42.323945 INFO::Fitting model to feature number 786, ASV_615
#> 2023-05-08 20:11:42.327007 INFO::Fitting model to feature number 787, ASV_2484
#> 2023-05-08 20:11:42.329969 INFO::Fitting model to feature number 788, ASV_1987
#> 2023-05-08 20:11:42.333754 INFO::Fitting model to feature number 789, ASV_2435
#> 2023-05-08 20:11:42.337243 INFO::Fitting model to feature number 790, ASV_2452
#> 2023-05-08 20:11:42.341278 INFO::Fitting model to feature number 791, ASV_1436
#> 2023-05-08 20:11:42.344525 INFO::Fitting model to feature number 792, ASV_2743
#> 2023-05-08 20:11:42.34804 INFO::Fitting model to feature number 793, ASV_2101
#> 2023-05-08 20:11:42.351911 INFO::Fitting model to feature number 794, ASV_1897
#> 2023-05-08 20:11:42.355547 INFO::Fitting model to feature number 795, ASV_2745
#> 2023-05-08 20:11:42.358732 INFO::Fitting model to feature number 796, ASV_2770
#> 2023-05-08 20:11:42.361881 INFO::Fitting model to feature number 797, ASV_2272
#> 2023-05-08 20:11:42.364897 INFO::Fitting model to feature number 798, ASV_306
#> 2023-05-08 20:11:42.367963 INFO::Fitting model to feature number 799, ASV_2301
#> 2023-05-08 20:11:42.371248 INFO::Fitting model to feature number 800, ASV_322
#> 2023-05-08 20:11:42.374387 INFO::Fitting model to feature number 801, ASV_498
#> 2023-05-08 20:11:42.377643 INFO::Fitting model to feature number 802, ASV_1763
#> 2023-05-08 20:11:42.380889 INFO::Fitting model to feature number 803, ASV_1068
#> 2023-05-08 20:11:42.383929 INFO::Fitting model to feature number 804, ASV_1208
#> 2023-05-08 20:11:42.387138 INFO::Fitting model to feature number 805, ASV_2823
#> 2023-05-08 20:11:42.390194 INFO::Fitting model to feature number 806, ASV_1116
#> 2023-05-08 20:11:42.393297 INFO::Fitting model to feature number 807, ASV_1921
#> 2023-05-08 20:11:42.396309 INFO::Fitting model to feature number 808, ASV_2915
#> 2023-05-08 20:11:42.399355 INFO::Fitting model to feature number 809, ASV_684
#> 2023-05-08 20:11:42.402543 INFO::Fitting model to feature number 810, ASV_850
#> 2023-05-08 20:11:42.405701 INFO::Fitting model to feature number 811, ASV_1359
#> 2023-05-08 20:11:42.408734 INFO::Fitting model to feature number 812, ASV_2491
#> 2023-05-08 20:11:42.411693 INFO::Fitting model to feature number 813, ASV_1396
#> 2023-05-08 20:11:42.414649 INFO::Fitting model to feature number 814, ASV_2388
#> 2023-05-08 20:11:42.418144 INFO::Fitting model to feature number 815, ASV_1526
#> 2023-05-08 20:11:42.42222 INFO::Fitting model to feature number 816, ASV_2632
#> 2023-05-08 20:11:42.425907 INFO::Fitting model to feature number 817, ASV_1880
#> 2023-05-08 20:11:42.429108 INFO::Fitting model to feature number 818, ASV_2810
#> 2023-05-08 20:11:42.432345 INFO::Fitting model to feature number 819, ASV_417
#> 2023-05-08 20:11:42.435459 INFO::Fitting model to feature number 820, ASV_535
#> 2023-05-08 20:11:42.438483 INFO::Fitting model to feature number 821, ASV_2007
#> 2023-05-08 20:11:42.441699 INFO::Fitting model to feature number 822, ASV_68
#> 2023-05-08 20:11:42.445104 INFO::Fitting model to feature number 823, ASV_142
#> 2023-05-08 20:11:42.44837 INFO::Fitting model to feature number 824, ASV_1214
#> 2023-05-08 20:11:42.452066 INFO::Fitting model to feature number 825, ASV_2824
#> 2023-05-08 20:11:42.455315 INFO::Fitting model to feature number 826, ASV_65
#> 2023-05-08 20:11:42.458438 INFO::Fitting model to feature number 827, ASV_456
#> 2023-05-08 20:11:42.461626 INFO::Fitting model to feature number 828, ASV_1111
#> 2023-05-08 20:11:42.464822 INFO::Fitting model to feature number 829, ASV_2105
#> 2023-05-08 20:11:42.467911 INFO::Fitting model to feature number 830, ASV_2537
#> 2023-05-08 20:11:42.471071 INFO::Fitting model to feature number 831, ASV_583
#> 2023-05-08 20:11:42.474279 INFO::Fitting model to feature number 832, ASV_722
#> 2023-05-08 20:11:42.477847 INFO::Fitting model to feature number 833, ASV_915
#> 2023-05-08 20:11:42.481137 INFO::Fitting model to feature number 834, ASV_1365
#> 2023-05-08 20:11:42.484439 INFO::Fitting model to feature number 835, ASV_2035
#> 2023-05-08 20:11:42.487705 INFO::Fitting model to feature number 836, ASV_858
#> 2023-05-08 20:11:42.490871 INFO::Fitting model to feature number 837, ASV_1322
#> 2023-05-08 20:11:42.494406 INFO::Fitting model to feature number 838, ASV_1816
#> 2023-05-08 20:11:42.498068 INFO::Fitting model to feature number 839, ASV_2086
#> 2023-05-08 20:11:42.501355 INFO::Fitting model to feature number 840, ASV_2351
#> 2023-05-08 20:11:42.504593 INFO::Fitting model to feature number 841, ASV_928
#> 2023-05-08 20:11:42.508182 INFO::Fitting model to feature number 842, ASV_56
#> 2023-05-08 20:11:42.512616 INFO::Fitting model to feature number 843, ASV_2201
#> 2023-05-08 20:11:42.516266 INFO::Fitting model to feature number 844, ASV_1001
#> 2023-05-08 20:11:42.520019 INFO::Fitting model to feature number 845, ASV_257
#> 2023-05-08 20:11:42.523391 INFO::Fitting model to feature number 846, ASV_1382
#> 2023-05-08 20:11:42.527418 INFO::Fitting model to feature number 847, ASV_2498
#> 2023-05-08 20:11:42.53114 INFO::Fitting model to feature number 848, ASV_339
#> 2023-05-08 20:11:42.534644 INFO::Fitting model to feature number 849, ASV_369
#> 2023-05-08 20:11:42.538114 INFO::Fitting model to feature number 850, ASV_611
#> 2023-05-08 20:11:42.554282 INFO::Fitting model to feature number 851, ASV_834
#> 2023-05-08 20:11:42.558748 INFO::Fitting model to feature number 852, ASV_1327
#> 2023-05-08 20:11:42.563049 INFO::Fitting model to feature number 853, ASV_2520
#> 2023-05-08 20:11:42.566866 INFO::Fitting model to feature number 854, ASV_269
#> 2023-05-08 20:11:42.570341 INFO::Fitting model to feature number 855, ASV_934
#> 2023-05-08 20:11:42.57432 INFO::Fitting model to feature number 856, ASV_331
#> 2023-05-08 20:11:42.577648 INFO::Fitting model to feature number 857, ASV_278
#> 2023-05-08 20:11:42.580965 INFO::Fitting model to feature number 858, ASV_1632
#> 2023-05-08 20:11:42.584307 INFO::Fitting model to feature number 859, ASV_282
#> 2023-05-08 20:11:42.588173 INFO::Fitting model to feature number 860, ASV_511
#> 2023-05-08 20:11:42.591962 INFO::Fitting model to feature number 861, ASV_402
#> 2023-05-08 20:11:42.595111 INFO::Fitting model to feature number 862, ASV_292
#> 2023-05-08 20:11:42.599138 INFO::Fitting model to feature number 863, ASV_96
#> 2023-05-08 20:11:42.602166 INFO::Fitting model to feature number 864, ASV_1601
#> 2023-05-08 20:11:42.605486 INFO::Fitting model to feature number 865, ASV_105
#> 2023-05-08 20:11:42.608537 INFO::Fitting model to feature number 866, ASV_573
#> 2023-05-08 20:11:42.611734 INFO::Fitting model to feature number 867, ASV_897
#> 2023-05-08 20:11:42.614705 INFO::Fitting model to feature number 868, ASV_2100
#> 2023-05-08 20:11:42.618138 INFO::Fitting model to feature number 869, ASV_1293
#> 2023-05-08 20:11:42.621383 INFO::Fitting model to feature number 870, ASV_1740
#> 2023-05-08 20:11:42.624624 INFO::Fitting model to feature number 871, ASV_1820
#> 2023-05-08 20:11:42.627633 INFO::Fitting model to feature number 872, ASV_2779
#> 2023-05-08 20:11:42.631108 INFO::Fitting model to feature number 873, ASV_1630
#> 2023-05-08 20:11:42.634553 INFO::Fitting model to feature number 874, ASV_2755
#> 2023-05-08 20:11:42.637672 INFO::Fitting model to feature number 875, ASV_2830
#> 2023-05-08 20:11:42.64073 INFO::Fitting model to feature number 876, ASV_267
#> 2023-05-08 20:11:42.644151 INFO::Fitting model to feature number 877, ASV_1521
#> 2023-05-08 20:11:42.64759 INFO::Fitting model to feature number 878, ASV_2006
#> 2023-05-08 20:11:42.652432 INFO::Fitting model to feature number 879, ASV_2326
#> 2023-05-08 20:11:42.655655 INFO::Fitting model to feature number 880, ASV_2074
#> 2023-05-08 20:11:42.658703 INFO::Fitting model to feature number 881, ASV_317
#> 2023-05-08 20:11:42.66175 INFO::Fitting model to feature number 882, ASV_644
#> 2023-05-08 20:11:42.665035 INFO::Fitting model to feature number 883, ASV_1518
#> 2023-05-08 20:11:42.668354 INFO::Fitting model to feature number 884, ASV_410
#> 2023-05-08 20:11:42.671586 INFO::Fitting model to feature number 885, ASV_1104
#> 2023-05-08 20:11:42.674867 INFO::Fitting model to feature number 886, ASV_1363
#> 2023-05-08 20:11:42.678252 INFO::Fitting model to feature number 887, ASV_1694
#> 2023-05-08 20:11:42.681522 INFO::Fitting model to feature number 888, ASV_478
#> 2023-05-08 20:11:42.68465 INFO::Fitting model to feature number 889, ASV_887
#> 2023-05-08 20:11:42.687672 INFO::Fitting model to feature number 890, ASV_304
#> 2023-05-08 20:11:42.690689 INFO::Fitting model to feature number 891, ASV_335
#> 2023-05-08 20:11:42.69374 INFO::Fitting model to feature number 892, ASV_1830
#> 2023-05-08 20:11:42.697119 INFO::Fitting model to feature number 893, ASV_325
#> 2023-05-08 20:11:42.700283 INFO::Fitting model to feature number 894, ASV_1124
#> 2023-05-08 20:11:42.703342 INFO::Fitting model to feature number 895, ASV_1702
#> 2023-05-08 20:11:42.706416 INFO::Fitting model to feature number 896, ASV_2854
#> 2023-05-08 20:11:42.709407 INFO::Fitting model to feature number 897, ASV_997
#> 2023-05-08 20:11:42.712632 INFO::Fitting model to feature number 898, ASV_1445
#> 2023-05-08 20:11:42.716171 INFO::Fitting model to feature number 899, ASV_2166
#> 2023-05-08 20:11:42.719437 INFO::Fitting model to feature number 900, ASV_24
#> 2023-05-08 20:11:42.722714 INFO::Fitting model to feature number 901, ASV_182
#> 2023-05-08 20:11:42.726133 INFO::Fitting model to feature number 902, ASV_1778
#> 2023-05-08 20:11:42.730151 INFO::Fitting model to feature number 903, ASV_545
#> 2023-05-08 20:11:42.733511 INFO::Fitting model to feature number 904, ASV_1094
#> 2023-05-08 20:11:42.736806 INFO::Fitting model to feature number 905, ASV_2542
#> 2023-05-08 20:11:42.740097 INFO::Fitting model to feature number 906, ASV_762
#> 2023-05-08 20:11:42.743603 INFO::Fitting model to feature number 907, ASV_2403
#> 2023-05-08 20:11:42.746831 INFO::Fitting model to feature number 908, ASV_902
#> 2023-05-08 20:11:42.749907 INFO::Fitting model to feature number 909, ASV_1551
#> 2023-05-08 20:11:42.753056 INFO::Fitting model to feature number 910, ASV_2237
#> 2023-05-08 20:11:42.756103 INFO::Fitting model to feature number 911, ASV_2569
#> 2023-05-08 20:11:42.759204 INFO::Fitting model to feature number 912, ASV_195
#> 2023-05-08 20:11:42.76221 INFO::Fitting model to feature number 913, ASV_301
#> 2023-05-08 20:11:42.765291 INFO::Fitting model to feature number 914, ASV_665
#> 2023-05-08 20:11:42.768328 INFO::Fitting model to feature number 915, ASV_1431
#> 2023-05-08 20:11:42.771317 INFO::Fitting model to feature number 916, ASV_2242
#> 2023-05-08 20:11:42.776878 INFO::Fitting model to feature number 917, ASV_681
#> 2023-05-08 20:11:42.780183 INFO::Fitting model to feature number 918, ASV_2436
#> 2023-05-08 20:11:42.783669 INFO::Fitting model to feature number 919, ASV_1050
#> 2023-05-08 20:11:42.787395 INFO::Fitting model to feature number 920, ASV_1139
#> 2023-05-08 20:11:42.791195 INFO::Fitting model to feature number 921, ASV_2273
#> 2023-05-08 20:11:42.794755 INFO::Fitting model to feature number 922, ASV_409
#> 2023-05-08 20:11:42.798204 INFO::Fitting model to feature number 923, ASV_1798
#> 2023-05-08 20:11:42.80176 INFO::Fitting model to feature number 924, ASV_1332
#> 2023-05-08 20:11:42.805244 INFO::Fitting model to feature number 925, ASV_560
#> 2023-05-08 20:11:42.808665 INFO::Fitting model to feature number 926, ASV_2131
#> 2023-05-08 20:11:42.811736 INFO::Fitting model to feature number 927, ASV_2864
#> 2023-05-08 20:11:42.814826 INFO::Fitting model to feature number 928, ASV_462
#> 2023-05-08 20:11:42.817894 INFO::Fitting model to feature number 929, ASV_1623
#> 2023-05-08 20:11:42.821176 INFO::Fitting model to feature number 930, ASV_2304
#> 2023-05-08 20:11:42.824365 INFO::Fitting model to feature number 931, ASV_2042
#> 2023-05-08 20:11:42.827717 INFO::Fitting model to feature number 932, ASV_379
#> 2023-05-08 20:11:42.831017 INFO::Fitting model to feature number 933, ASV_1317
#> 2023-05-08 20:11:42.834171 INFO::Fitting model to feature number 934, ASV_1529
#> 2023-05-08 20:11:42.837739 INFO::Fitting model to feature number 935, ASV_775
#> 2023-05-08 20:11:42.841002 INFO::Fitting model to feature number 936, ASV_2692
#> 2023-05-08 20:11:42.844099 INFO::Fitting model to feature number 937, ASV_2501
#> 2023-05-08 20:11:42.847117 INFO::Fitting model to feature number 938, ASV_562
#> 2023-05-08 20:11:42.850155 INFO::Fitting model to feature number 939, ASV_639
#> 2023-05-08 20:11:42.853296 INFO::Fitting model to feature number 940, ASV_1176
#> 2023-05-08 20:11:42.856517 INFO::Fitting model to feature number 941, ASV_2525
#> 2023-05-08 20:11:42.859547 INFO::Fitting model to feature number 942, ASV_1441
#> 2023-05-08 20:11:42.86261 INFO::Fitting model to feature number 943, ASV_1000
#> 2023-05-08 20:11:42.865741 INFO::Fitting model to feature number 944, ASV_1243
#> 2023-05-08 20:11:42.86941 INFO::Fitting model to feature number 945, ASV_71
#> 2023-05-08 20:11:42.873372 INFO::Fitting model to feature number 946, ASV_179
#> 2023-05-08 20:11:42.876698 INFO::Fitting model to feature number 947, ASV_2334
#> 2023-05-08 20:11:42.879955 INFO::Fitting model to feature number 948, ASV_861
#> 2023-05-08 20:11:42.883528 INFO::Fitting model to feature number 949, ASV_176
#> 2023-05-08 20:11:42.88696 INFO::Fitting model to feature number 950, ASV_2908
#> 2023-05-08 20:11:42.890309 INFO::Fitting model to feature number 951, ASV_1375
#> 2023-05-08 20:11:42.893608 INFO::Fitting model to feature number 952, ASV_1286
#> 2023-05-08 20:11:42.897043 INFO::Fitting model to feature number 953, ASV_2673
#> 2023-05-08 20:11:42.900699 INFO::Fitting model to feature number 954, ASV_140
#> 2023-05-08 20:11:42.903919 INFO::Fitting model to feature number 955, ASV_578
#> 2023-05-08 20:11:42.906927 INFO::Fitting model to feature number 956, ASV_626
#> 2023-05-08 20:11:42.910429 INFO::Fitting model to feature number 957, ASV_956
#> 2023-05-08 20:11:42.913555 INFO::Fitting model to feature number 958, ASV_1095
#> 2023-05-08 20:11:42.916917 INFO::Fitting model to feature number 959, ASV_1334
#> 2023-05-08 20:11:42.920033 INFO::Fitting model to feature number 960, ASV_1096
#> 2023-05-08 20:11:42.923028 INFO::Fitting model to feature number 961, ASV_1713
#> 2023-05-08 20:11:42.926062 INFO::Fitting model to feature number 962, ASV_2397
#> 2023-05-08 20:11:42.929104 INFO::Fitting model to feature number 963, ASV_2463
#> 2023-05-08 20:11:42.932338 INFO::Fitting model to feature number 964, ASV_856
#> 2023-05-08 20:11:42.935375 INFO::Fitting model to feature number 965, ASV_1412
#> 2023-05-08 20:11:42.938352 INFO::Fitting model to feature number 966, ASV_2198
#> 2023-05-08 20:11:42.941316 INFO::Fitting model to feature number 967, ASV_385
#> 2023-05-08 20:11:42.944312 INFO::Fitting model to feature number 968, ASV_2292
#> 2023-05-08 20:11:42.947795 INFO::Fitting model to feature number 969, ASV_460
#> 2023-05-08 20:11:42.950983 INFO::Fitting model to feature number 970, ASV_1913
#> 2023-05-08 20:11:42.954224 INFO::Fitting model to feature number 971, ASV_2025
#> 2023-05-08 20:11:42.957619 INFO::Fitting model to feature number 972, ASV_29
#> 2023-05-08 20:11:42.961055 INFO::Fitting model to feature number 973, ASV_129
#> 2023-05-08 20:11:42.964545 INFO::Fitting model to feature number 974, ASV_477
#> 2023-05-08 20:11:42.967818 INFO::Fitting model to feature number 975, ASV_1983
#> 2023-05-08 20:11:42.971011 INFO::Fitting model to feature number 976, ASV_540
#> 2023-05-08 20:11:42.974323 INFO::Fitting model to feature number 977, ASV_1804
#> 2023-05-08 20:11:42.97779 INFO::Fitting model to feature number 978, ASV_704
#> 2023-05-08 20:11:42.980982 INFO::Fitting model to feature number 979, ASV_1523
#> 2023-05-08 20:11:42.984102 INFO::Fitting model to feature number 980, ASV_2885
#> 2023-05-08 20:11:42.987096 INFO::Fitting model to feature number 981, ASV_1871
#> 2023-05-08 20:11:42.990078 INFO::Fitting model to feature number 982, ASV_2551
#> 2023-05-08 20:11:42.993225 INFO::Fitting model to feature number 983, ASV_2029
#> 2023-05-08 20:11:42.996302 INFO::Fitting model to feature number 984, ASV_2504
#> 2023-05-08 20:11:42.999419 INFO::Fitting model to feature number 985, ASV_2798
#> 2023-05-08 20:11:43.002428 INFO::Fitting model to feature number 986, ASV_2257
#> 2023-05-08 20:11:43.005434 INFO::Fitting model to feature number 987, ASV_2535
#> 2023-05-08 20:11:43.009022 INFO::Fitting model to feature number 988, ASV_33
#> 2023-05-08 20:11:43.01251 INFO::Fitting model to feature number 989, ASV_203
#> 2023-05-08 20:11:43.015705 INFO::Fitting model to feature number 990, ASV_37
#> 2023-05-08 20:11:43.018691 INFO::Fitting model to feature number 991, ASV_259
#> 2023-05-08 20:11:43.021625 INFO::Fitting model to feature number 992, ASV_1756
#> 2023-05-08 20:11:43.025085 INFO::Fitting model to feature number 993, ASV_424
#> 2023-05-08 20:11:43.028553 INFO::Fitting model to feature number 994, ASV_580
#> 2023-05-08 20:11:43.031795 INFO::Fitting model to feature number 995, ASV_598
#> 2023-05-08 20:11:43.034832 INFO::Fitting model to feature number 996, ASV_734
#> 2023-05-08 20:11:43.037875 INFO::Fitting model to feature number 997, ASV_1724
#> 2023-05-08 20:11:43.041227 INFO::Fitting model to feature number 998, ASV_1538
#> 2023-05-08 20:11:43.044295 INFO::Fitting model to feature number 999, ASV_1903
#> 2023-05-08 20:11:43.047286 INFO::Fitting model to feature number 1000, ASV_2706
#> 2023-05-08 20:11:43.050257 INFO::Fitting model to feature number 1001, ASV_2412
#> 2023-05-08 20:11:43.053327 INFO::Fitting model to feature number 1002, ASV_1888
#> 2023-05-08 20:11:43.056596 INFO::Fitting model to feature number 1003, ASV_1950
#> 2023-05-08 20:11:43.059714 INFO::Fitting model to feature number 1004, ASV_2341
#> 2023-05-08 20:11:43.062792 INFO::Fitting model to feature number 1005, ASV_2536
#> 2023-05-08 20:11:43.065796 INFO::Fitting model to feature number 1006, ASV_1248
#> 2023-05-08 20:11:43.068873 INFO::Fitting model to feature number 1007, ASV_2560
#> 2023-05-08 20:11:43.071964 INFO::Fitting model to feature number 1008, ASV_2728
#> 2023-05-08 20:11:43.0752 INFO::Fitting model to feature number 1009, ASV_102
#> 2023-05-08 20:11:43.078649 INFO::Fitting model to feature number 1010, ASV_347
#> 2023-05-08 20:11:43.081801 INFO::Fitting model to feature number 1011, ASV_453
#> 2023-05-08 20:11:43.084814 INFO::Fitting model to feature number 1012, ASV_967
#> 2023-05-08 20:11:43.08806 INFO::Fitting model to feature number 1013, ASV_1119
#> 2023-05-08 20:11:43.091147 INFO::Fitting model to feature number 1014, ASV_1770
#> 2023-05-08 20:11:43.094131 INFO::Fitting model to feature number 1015, ASV_487
#> 2023-05-08 20:11:43.09717 INFO::Fitting model to feature number 1016, ASV_653
#> 2023-05-08 20:11:43.100159 INFO::Fitting model to feature number 1017, ASV_912
#> 2023-05-08 20:11:43.104595 INFO::Fitting model to feature number 1018, ASV_1736
#> 2023-05-08 20:11:43.108143 INFO::Fitting model to feature number 1019, ASV_398
#> 2023-05-08 20:11:43.111347 INFO::Fitting model to feature number 1020, ASV_1494
#> 2023-05-08 20:11:43.114454 INFO::Fitting model to feature number 1021, ASV_2486
#> 2023-05-08 20:11:43.117698 INFO::Fitting model to feature number 1022, ASV_831
#> 2023-05-08 20:11:43.120861 INFO::Fitting model to feature number 1023, ASV_294
#> 2023-05-08 20:11:43.123954 INFO::Fitting model to feature number 1024, ASV_522
#> 2023-05-08 20:11:43.127544 INFO::Fitting model to feature number 1025, ASV_552
#> 2023-05-08 20:11:43.130967 INFO::Fitting model to feature number 1026, ASV_1305
#> 2023-05-08 20:11:43.134915 INFO::Fitting model to feature number 1027, ASV_2110
#> 2023-05-08 20:11:43.138279 INFO::Fitting model to feature number 1028, ASV_530
#> 2023-05-08 20:11:43.14164 INFO::Fitting model to feature number 1029, ASV_745
#> 2023-05-08 20:11:43.144894 INFO::Fitting model to feature number 1030, ASV_1167
#> 2023-05-08 20:11:43.147856 INFO::Fitting model to feature number 1031, ASV_1390
#> 2023-05-08 20:11:43.151356 INFO::Fitting model to feature number 1032, ASV_499
#> 2023-05-08 20:11:43.154692 INFO::Fitting model to feature number 1033, ASV_1309
#> 2023-05-08 20:11:43.158027 INFO::Fitting model to feature number 1034, ASV_1474
#> 2023-05-08 20:11:43.161382 INFO::Fitting model to feature number 1035, ASV_754
#> 2023-05-08 20:11:43.16485 INFO::Fitting model to feature number 1036, ASV_2524
#> 2023-05-08 20:11:43.168016 INFO::Fitting model to feature number 1037, ASV_2348
#> 2023-05-08 20:11:43.171289 INFO::Fitting model to feature number 1038, ASV_46
#> 2023-05-08 20:11:43.174798 INFO::Fitting model to feature number 1039, ASV_119
#> 2023-05-08 20:11:43.178975 INFO::Fitting model to feature number 1040, ASV_153
#> 2023-05-08 20:11:43.182739 INFO::Fitting model to feature number 1041, ASV_227
#> 2023-05-08 20:11:43.186065 INFO::Fitting model to feature number 1042, ASV_401
#> 2023-05-08 20:11:43.18922 INFO::Fitting model to feature number 1043, ASV_954
#> 2023-05-08 20:11:43.19247 INFO::Fitting model to feature number 1044, ASV_285
#> 2023-05-08 20:11:43.196064 INFO::Fitting model to feature number 1045, ASV_642
#> 2023-05-08 20:11:43.19929 INFO::Fitting model to feature number 1046, ASV_1828
#> 2023-05-08 20:11:43.202511 INFO::Fitting model to feature number 1047, ASV_1876
#> 2023-05-08 20:11:43.205563 INFO::Fitting model to feature number 1048, ASV_2400
#> 2023-05-08 20:11:43.20859 INFO::Fitting model to feature number 1049, ASV_2853
#> 2023-05-08 20:11:43.211816 INFO::Fitting model to feature number 1050, ASV_77
#> 2023-05-08 20:11:43.21496 INFO::Fitting model to feature number 1051, ASV_181
#> 2023-05-08 20:11:43.218041 INFO::Fitting model to feature number 1052, ASV_362
#> 2023-05-08 20:11:43.221102 INFO::Fitting model to feature number 1053, ASV_435
#> 2023-05-08 20:11:43.224137 INFO::Fitting model to feature number 1054, ASV_213
#> 2023-05-08 20:11:43.22778 INFO::Fitting model to feature number 1055, ASV_423
#> 2023-05-08 20:11:43.231084 INFO::Fitting model to feature number 1056, ASV_383
#> 2023-05-08 20:11:43.234303 INFO::Fitting model to feature number 1057, ASV_1177
#> 2023-05-08 20:11:43.237308 INFO::Fitting model to feature number 1058, ASV_1029
#> 2023-05-08 20:11:43.240403 INFO::Fitting model to feature number 1059, ASV_283
#> 2023-05-08 20:11:43.24352 INFO::Fitting model to feature number 1060, ASV_966
#> 2023-05-08 20:11:43.246784 INFO::Fitting model to feature number 1061, ASV_1934
#> 2023-05-08 20:11:43.249842 INFO::Fitting model to feature number 1062, ASV_760
#> 2023-05-08 20:11:43.253247 INFO::Fitting model to feature number 1063, ASV_805
#> 2023-05-08 20:11:43.25707 INFO::Fitting model to feature number 1064, ASV_1904
#> 2023-05-08 20:11:43.260425 INFO::Fitting model to feature number 1065, ASV_265
#> 2023-05-08 20:11:43.263657 INFO::Fitting model to feature number 1066, ASV_327
#> 2023-05-08 20:11:43.266741 INFO::Fitting model to feature number 1067, ASV_439
#> 2023-05-08 20:11:43.269774 INFO::Fitting model to feature number 1068, ASV_1944
#> 2023-05-08 20:11:43.272989 INFO::Fitting model to feature number 1069, ASV_2669
#> 2023-05-08 20:11:43.276521 INFO::Fitting model to feature number 1070, ASV_1522
#> 2023-05-08 20:11:43.279665 INFO::Fitting model to feature number 1071, ASV_1567
#> 2023-05-08 20:11:43.282782 INFO::Fitting model to feature number 1072, ASV_1608
#> 2023-05-08 20:11:43.285799 INFO::Fitting model to feature number 1073, ASV_89
#> 2023-05-08 20:11:43.288982 INFO::Fitting model to feature number 1074, ASV_1247
#> 2023-05-08 20:11:43.292058 INFO::Fitting model to feature number 1075, ASV_360
#> 2023-05-08 20:11:43.29522 INFO::Fitting model to feature number 1076, ASV_1475
#> 2023-05-08 20:11:43.298566 INFO::Fitting model to feature number 1077, ASV_276
#> 2023-05-08 20:11:43.301822 INFO::Fitting model to feature number 1078, ASV_918
#> 2023-05-08 20:11:43.305283 INFO::Fitting model to feature number 1079, ASV_2734
#> 2023-05-08 20:11:43.308585 INFO::Fitting model to feature number 1080, ASV_938
#> 2023-05-08 20:11:43.311816 INFO::Fitting model to feature number 1081, ASV_1589
#> 2023-05-08 20:11:43.314951 INFO::Fitting model to feature number 1082, ASV_2382
#> 2023-05-08 20:11:43.318017 INFO::Fitting model to feature number 1083, ASV_1753
#> 2023-05-08 20:11:43.321545 INFO::Fitting model to feature number 1084, ASV_164
#> 2023-05-08 20:11:43.324828 INFO::Fitting model to feature number 1085, ASV_618
#> 2023-05-08 20:11:43.32805 INFO::Fitting model to feature number 1086, ASV_892
#> 2023-05-08 20:11:43.331157 INFO::Fitting model to feature number 1087, ASV_2568
#> 2023-05-08 20:11:43.334516 INFO::Fitting model to feature number 1088, ASV_794
#> 2023-05-08 20:11:43.338091 INFO::Fitting model to feature number 1089, ASV_1831
#> 2023-05-08 20:11:43.341604 INFO::Fitting model to feature number 1090, ASV_2507
#> 2023-05-08 20:11:43.344785 INFO::Fitting model to feature number 1091, ASV_528
#> 2023-05-08 20:11:43.347929 INFO::Fitting model to feature number 1092, ASV_1137
#> 2023-05-08 20:11:43.351434 INFO::Fitting model to feature number 1093, ASV_1588
#> 2023-05-08 20:11:43.354621 INFO::Fitting model to feature number 1094, ASV_1680
#> 2023-05-08 20:11:43.357996 INFO::Fitting model to feature number 1095, ASV_2857
#> 2023-05-08 20:11:43.361222 INFO::Fitting model to feature number 1096, ASV_577
#> 2023-05-08 20:11:43.364306 INFO::Fitting model to feature number 1097, ASV_702
#> 2023-05-08 20:11:43.367651 INFO::Fitting model to feature number 1098, ASV_34
#> 2023-05-08 20:11:43.370819 INFO::Fitting model to feature number 1099, ASV_1227
#> 2023-05-08 20:11:43.373912 INFO::Fitting model to feature number 1100, ASV_186
#> 2023-05-08 20:11:43.376969 INFO::Fitting model to feature number 1101, ASV_346
#> 2023-05-08 20:11:43.380005 INFO::Fitting model to feature number 1102, ASV_623
#> 2023-05-08 20:11:43.383165 INFO::Fitting model to feature number 1103, ASV_494
#> 2023-05-08 20:11:43.386209 INFO::Fitting model to feature number 1104, ASV_2144
#> 2023-05-08 20:11:43.389216 INFO::Fitting model to feature number 1105, ASV_264
#> 2023-05-08 20:11:43.392231 INFO::Fitting model to feature number 1106, ASV_950
#> 2023-05-08 20:11:43.395306 INFO::Fitting model to feature number 1107, ASV_884
#> 2023-05-08 20:11:43.398694 INFO::Fitting model to feature number 1108, ASV_2432
#> 2023-05-08 20:11:43.402572 INFO::Fitting model to feature number 1109, ASV_2738
#> 2023-05-08 20:11:43.40593 INFO::Fitting model to feature number 1110, ASV_2836
#> 2023-05-08 20:11:43.409487 INFO::Fitting model to feature number 1111, ASV_95
#> 2023-05-08 20:11:43.412642 INFO::Fitting model to feature number 1112, ASV_138
#> 2023-05-08 20:11:43.416004 INFO::Fitting model to feature number 1113, ASV_427
#> 2023-05-08 20:11:43.419127 INFO::Fitting model to feature number 1114, ASV_971
#> 2023-05-08 20:11:43.422545 INFO::Fitting model to feature number 1115, ASV_1808
#> 2023-05-08 20:11:43.426181 INFO::Fitting model to feature number 1116, ASV_1821
#> 2023-05-08 20:11:43.429729 INFO::Fitting model to feature number 1117, ASV_692
#> 2023-05-08 20:11:43.432946 INFO::Fitting model to feature number 1118, ASV_1602
#> 2023-05-08 20:11:43.435996 INFO::Fitting model to feature number 1119, ASV_1701
#> 2023-05-08 20:11:43.439045 INFO::Fitting model to feature number 1120, ASV_1819
#> 2023-05-08 20:11:43.442044 INFO::Fitting model to feature number 1121, ASV_1952
#> 2023-05-08 20:11:43.445284 INFO::Fitting model to feature number 1122, ASV_2043
#> 2023-05-08 20:11:43.448507 INFO::Fitting model to feature number 1123, ASV_1923
#> 2023-05-08 20:11:43.451726 INFO::Fitting model to feature number 1124, ASV_2805
#> 2023-05-08 20:11:43.454833 INFO::Fitting model to feature number 1125, ASV_2909
#> 2023-05-08 20:11:43.457975 INFO::Fitting model to feature number 1126, ASV_1226
#> 2023-05-08 20:11:43.461558 INFO::Fitting model to feature number 1127, ASV_2379
#> 2023-05-08 20:11:43.465058 INFO::Fitting model to feature number 1128, ASV_2679
#> 2023-05-08 20:11:43.468416 INFO::Fitting model to feature number 1129, ASV_2832
#> 2023-05-08 20:11:43.471916 INFO::Fitting model to feature number 1130, ASV_1085
#> 2023-05-08 20:11:43.475943 INFO::Fitting model to feature number 1131, ASV_480
#> 2023-05-08 20:11:43.479975 INFO::Fitting model to feature number 1132, ASV_781
#> 2023-05-08 20:11:43.483152 INFO::Fitting model to feature number 1133, ASV_1009
#> 2023-05-08 20:11:43.486439 INFO::Fitting model to feature number 1134, ASV_2437
#> 2023-05-08 20:11:43.489492 INFO::Fitting model to feature number 1135, ASV_2470
#> 2023-05-08 20:11:43.492841 INFO::Fitting model to feature number 1136, ASV_1674
#> 2023-05-08 20:11:43.495908 INFO::Fitting model to feature number 1137, ASV_1948
#> 2023-05-08 20:11:43.499022 INFO::Fitting model to feature number 1138, ASV_741
#> 2023-05-08 20:11:43.502109 INFO::Fitting model to feature number 1139, ASV_2508
#> 2023-05-08 20:11:43.505147 INFO::Fitting model to feature number 1140, ASV_1299
#> 2023-05-08 20:11:43.508625 INFO::Fitting model to feature number 1141, ASV_2841
#> 2023-05-08 20:11:43.511957 INFO::Fitting model to feature number 1142, ASV_2665
#> 2023-05-08 20:11:43.515092 INFO::Fitting model to feature number 1143, ASV_738
#> 2023-05-08 20:11:43.518268 INFO::Fitting model to feature number 1144, ASV_2214
#> 2023-05-08 20:11:43.521458 INFO::Fitting model to feature number 1145, ASV_2270
#> 2023-05-08 20:11:43.524616 INFO::Fitting model to feature number 1146, ASV_764
#> 2023-05-08 20:11:43.527793 INFO::Fitting model to feature number 1147, ASV_2716
#> 2023-05-08 20:11:43.53092 INFO::Fitting model to feature number 1148, ASV_2313
#> 2023-05-08 20:11:43.533996 INFO::Fitting model to feature number 1149, ASV_1311
#> 2023-05-08 20:11:43.536998 INFO::Fitting model to feature number 1150, ASV_2236
#> 2023-05-08 20:11:43.540504 INFO::Fitting model to feature number 1151, ASV_53
#> 2023-05-08 20:11:43.543757 INFO::Fitting model to feature number 1152, ASV_178
#> 2023-05-08 20:11:43.547294 INFO::Fitting model to feature number 1153, ASV_464
#> 2023-05-08 20:11:43.550535 INFO::Fitting model to feature number 1154, ASV_113
#> 2023-05-08 20:11:43.553932 INFO::Fitting model to feature number 1155, ASV_219
#> 2023-05-08 20:11:43.557331 INFO::Fitting model to feature number 1156, ASV_814
#> 2023-05-08 20:11:43.56047 INFO::Fitting model to feature number 1157, ASV_1152
#> 2023-05-08 20:11:43.563618 INFO::Fitting model to feature number 1158, ASV_2911
#> 2023-05-08 20:11:43.566754 INFO::Fitting model to feature number 1159, ASV_542
#> 2023-05-08 20:11:43.570159 INFO::Fitting model to feature number 1160, ASV_1495
#> 2023-05-08 20:11:43.573507 INFO::Fitting model to feature number 1161, ASV_1048
#> 2023-05-08 20:11:43.576645 INFO::Fitting model to feature number 1162, ASV_1777
#> 2023-05-08 20:11:43.579635 INFO::Fitting model to feature number 1163, ASV_2169
#> 2023-05-08 20:11:43.582844 INFO::Fitting model to feature number 1164, ASV_198
#> 2023-05-08 20:11:43.586263 INFO::Fitting model to feature number 1165, ASV_262
#> 2023-05-08 20:11:43.589474 INFO::Fitting model to feature number 1166, ASV_668
#> 2023-05-08 20:11:43.592958 INFO::Fitting model to feature number 1167, ASV_364
#> 2023-05-08 20:11:43.596082 INFO::Fitting model to feature number 1168, ASV_839
#> 2023-05-08 20:11:43.599192 INFO::Fitting model to feature number 1169, ASV_1019
#> 2023-05-08 20:11:43.602419 INFO::Fitting model to feature number 1170, ASV_1698
#> 2023-05-08 20:11:43.605511 INFO::Fitting model to feature number 1171, ASV_452
#> 2023-05-08 20:11:43.608819 INFO::Fitting model to feature number 1172, ASV_1053
#> 2023-05-08 20:11:43.611858 INFO::Fitting model to feature number 1173, ASV_660
#> 2023-05-08 20:11:43.615322 INFO::Fitting model to feature number 1174, ASV_2530
#> 2023-05-08 20:11:43.618518 INFO::Fitting model to feature number 1175, ASV_2709
#> 2023-05-08 20:11:43.621508 INFO::Fitting model to feature number 1176, ASV_35
#> 2023-05-08 20:11:43.62447 INFO::Fitting model to feature number 1177, ASV_2651
#> 2023-05-08 20:11:43.627463 INFO::Fitting model to feature number 1178, ASV_2689
#> 2023-05-08 20:11:43.63042 INFO::Fitting model to feature number 1179, ASV_2811
#> 2023-05-08 20:11:43.633603 INFO::Fitting model to feature number 1180, ASV_1223
#> 2023-05-08 20:11:43.637416 INFO::Fitting model to feature number 1181, ASV_2601
#> 2023-05-08 20:11:43.640748 INFO::Fitting model to feature number 1182, ASV_2666
#> 2023-05-08 20:11:43.644144 INFO::Fitting model to feature number 1183, ASV_1512
#> 2023-05-08 20:11:43.64737 INFO::Fitting model to feature number 1184, ASV_1898
#> 2023-05-08 20:11:43.650779 INFO::Fitting model to feature number 1185, ASV_214
#> 2023-05-08 20:11:43.653931 INFO::Fitting model to feature number 1186, ASV_1618
#> 2023-05-08 20:11:43.657097 INFO::Fitting model to feature number 1187, ASV_222
#> 2023-05-08 20:11:43.660217 INFO::Fitting model to feature number 1188, ASV_1427
#> 2023-05-08 20:11:43.663247 INFO::Fitting model to feature number 1189, ASV_241
#> 2023-05-08 20:11:43.666557 INFO::Fitting model to feature number 1190, ASV_844
#> 2023-05-08 20:11:43.669632 INFO::Fitting model to feature number 1191, ASV_2628
#> 2023-05-08 20:11:43.672588 INFO::Fitting model to feature number 1192, ASV_1063
#> 2023-05-08 20:11:43.675577 INFO::Fitting model to feature number 1193, ASV_245
#> 2023-05-08 20:11:43.678526 INFO::Fitting model to feature number 1194, ASV_1341
#> 2023-05-08 20:11:43.68184 INFO::Fitting model to feature number 1195, ASV_367
#> 2023-05-08 20:11:43.68496 INFO::Fitting model to feature number 1196, ASV_591
#> 2023-05-08 20:11:43.688045 INFO::Fitting model to feature number 1197, ASV_2031
#> 2023-05-08 20:11:43.691433 INFO::Fitting model to feature number 1198, ASV_724
#> 2023-05-08 20:11:43.694829 INFO::Fitting model to feature number 1199, ASV_1012
#> 2023-05-08 20:11:43.699022 INFO::Fitting model to feature number 1200, ASV_455
#> 2023-05-08 20:11:43.702852 INFO::Fitting model to feature number 1201, ASV_1082
#> 2023-05-08 20:11:43.706128 INFO::Fitting model to feature number 1202, ASV_680
#> 2023-05-08 20:11:43.70918 INFO::Fitting model to feature number 1203, ASV_1021
#> 2023-05-08 20:11:43.712146 INFO::Fitting model to feature number 1204, ASV_1625
#> 2023-05-08 20:11:43.715643 INFO::Fitting model to feature number 1205, ASV_906
#> 2023-05-08 20:11:43.718858 INFO::Fitting model to feature number 1206, ASV_152
#> 2023-05-08 20:11:43.721866 INFO::Fitting model to feature number 1207, ASV_239
#> 2023-05-08 20:11:43.724833 INFO::Fitting model to feature number 1208, ASV_589
#> 2023-05-08 20:11:43.727759 INFO::Fitting model to feature number 1209, ASV_1143
#> 2023-05-08 20:11:43.730836 INFO::Fitting model to feature number 1210, ASV_1546
#> 2023-05-08 20:11:43.733784 INFO::Fitting model to feature number 1211, ASV_2269
#> 2023-05-08 20:11:43.736682 INFO::Fitting model to feature number 1212, ASV_2076
#> 2023-05-08 20:11:43.73965 INFO::Fitting model to feature number 1213, ASV_2917
#> 2023-05-08 20:11:43.742831 INFO::Fitting model to feature number 1214, ASV_983
#> 2023-05-08 20:11:43.746105 INFO::Fitting model to feature number 1215, ASV_221
#> 2023-05-08 20:11:43.749157 INFO::Fitting model to feature number 1216, ASV_880
#> 2023-05-08 20:11:43.752159 INFO::Fitting model to feature number 1217, ASV_1186
#> 2023-05-08 20:11:43.755261 INFO::Fitting model to feature number 1218, ASV_40
#> 2023-05-08 20:11:43.758631 INFO::Fitting model to feature number 1219, ASV_78
#> 2023-05-08 20:11:43.761919 INFO::Fitting model to feature number 1220, ASV_1563
#> 2023-05-08 20:11:43.765085 INFO::Fitting model to feature number 1221, ASV_2092
#> 2023-05-08 20:11:43.768281 INFO::Fitting model to feature number 1222, ASV_386
#> 2023-05-08 20:11:43.772356 INFO::Fitting model to feature number 1223, ASV_1108
#> 2023-05-08 20:11:43.776681 INFO::Fitting model to feature number 1224, ASV_1980
#> 2023-05-08 20:11:43.779967 INFO::Fitting model to feature number 1225, ASV_1517
#> 2023-05-08 20:11:43.783425 INFO::Fitting model to feature number 1226, ASV_2912
#> 2023-05-08 20:11:43.78703 INFO::Fitting model to feature number 1227, ASV_43
#> 2023-05-08 20:11:43.79041 INFO::Fitting model to feature number 1228, ASV_172
#> 2023-05-08 20:11:43.793761 INFO::Fitting model to feature number 1229, ASV_458
#> 2023-05-08 20:11:43.7969 INFO::Fitting model to feature number 1230, ASV_376
#> 2023-05-08 20:11:43.799937 INFO::Fitting model to feature number 1231, ASV_1380
#> 2023-05-08 20:11:43.803049 INFO::Fitting model to feature number 1232, ASV_1083
#> 2023-05-08 20:11:43.806161 INFO::Fitting model to feature number 1233, ASV_1230
#> 2023-05-08 20:11:43.809615 INFO::Fitting model to feature number 1234, ASV_1093
#> 2023-05-08 20:11:43.812732 INFO::Fitting model to feature number 1235, ASV_1742
#> 2023-05-08 20:11:43.815767 INFO::Fitting model to feature number 1236, ASV_767
#> 2023-05-08 20:11:43.818832 INFO::Fitting model to feature number 1237, ASV_1612
#> 2023-05-08 20:11:43.821811 INFO::Fitting model to feature number 1238, ASV_942
#> 2023-05-08 20:11:43.824821 INFO::Fitting model to feature number 1239, ASV_2118
#> 2023-05-08 20:11:43.827793 INFO::Fitting model to feature number 1240, ASV_1497
#> 2023-05-08 20:11:43.831258 INFO::Fitting model to feature number 1241, ASV_1041
#> 2023-05-08 20:11:43.834455 INFO::Fitting model to feature number 1242, ASV_1764
#> 2023-05-08 20:11:43.837595 INFO::Fitting model to feature number 1243, ASV_1977
#> 2023-05-08 20:11:43.841017 INFO::Fitting model to feature number 1244, ASV_798
#> 2023-05-08 20:11:43.844103 INFO::Fitting model to feature number 1245, ASV_1858
#> 2023-05-08 20:11:43.847114 INFO::Fitting model to feature number 1246, ASV_1166
#> 2023-05-08 20:11:43.850142 INFO::Fitting model to feature number 1247, ASV_246
#> 2023-05-08 20:11:43.853169 INFO::Fitting model to feature number 1248, ASV_916
#> 2023-05-08 20:11:43.856618 INFO::Fitting model to feature number 1249, ASV_2647
#> 2023-05-08 20:11:43.860242 INFO::Fitting model to feature number 1250, ASV_1103
#> 2023-05-08 20:11:43.863515 INFO::Fitting model to feature number 1251, ASV_2859
#> 2023-05-08 20:11:43.866579 INFO::Fitting model to feature number 1252, ASV_1955
#> 2023-05-08 20:11:43.869573 INFO::Fitting model to feature number 1253, ASV_2399
#> 2023-05-08 20:11:43.873174 INFO::Fitting model to feature number 1254, ASV_2340
#> 2023-05-08 20:11:43.876838 INFO::Fitting model to feature number 1255, ASV_2797
#> 2023-05-08 20:11:43.880296 INFO::Fitting model to feature number 1256, ASV_790
#> 2023-05-08 20:11:43.88351 INFO::Fitting model to feature number 1257, ASV_1681
#> 2023-05-08 20:11:43.886702 INFO::Fitting model to feature number 1258, ASV_1658
#> 2023-05-08 20:11:43.889903 INFO::Fitting model to feature number 1259, ASV_2136
#> 2023-05-08 20:11:43.893213 INFO::Fitting model to feature number 1260, ASV_2170
#> 2023-05-08 20:11:43.89638 INFO::Fitting model to feature number 1261, ASV_1052
#> 2023-05-08 20:11:43.899486 INFO::Fitting model to feature number 1262, ASV_2264
#> 2023-05-08 20:11:43.902749 INFO::Fitting model to feature number 1263, ASV_2766
#> 2023-05-08 20:11:43.905928 INFO::Fitting model to feature number 1264, ASV_1148
#> 2023-05-08 20:11:43.909066 INFO::Fitting model to feature number 1265, ASV_1870
#> 2023-05-08 20:11:43.9121 INFO::Fitting model to feature number 1266, ASV_1047
#> 2023-05-08 20:11:43.915303 INFO::Fitting model to feature number 1267, ASV_664
#> 2023-05-08 20:11:43.918621 INFO::Fitting model to feature number 1268, ASV_2906
#> 2023-05-08 20:11:43.921719 INFO::Fitting model to feature number 1269, ASV_2519
#> 2023-05-08 20:11:43.925153 INFO::Fitting model to feature number 1270, ASV_2350
#> 2023-05-08 20:11:43.928309 INFO::Fitting model to feature number 1271, ASV_1846
#> 2023-05-08 20:11:43.931372 INFO::Fitting model to feature number 1272, ASV_2152
#> 2023-05-08 20:11:43.934875 INFO::Fitting model to feature number 1273, ASV_1013
#> 2023-05-08 20:11:43.938548 INFO::Fitting model to feature number 1274, ASV_1106
#> 2023-05-08 20:11:43.941785 INFO::Fitting model to feature number 1275, ASV_1405
#> 2023-05-08 20:11:43.944905 INFO::Fitting model to feature number 1276, ASV_2249
#> 2023-05-08 20:11:43.947996 INFO::Fitting model to feature number 1277, ASV_1002
#> 2023-05-08 20:11:43.951336 INFO::Fitting model to feature number 1278, ASV_1244
#> 2023-05-08 20:11:43.954408 INFO::Fitting model to feature number 1279, ASV_1150
#> 2023-05-08 20:11:43.957465 INFO::Fitting model to feature number 1280, ASV_1853
#> 2023-05-08 20:11:43.960484 INFO::Fitting model to feature number 1281, ASV_2282
#> 2023-05-08 20:11:43.963495 INFO::Fitting model to feature number 1282, ASV_2916
#> 2023-05-08 20:11:43.966661 INFO::Fitting model to feature number 1283, ASV_1234
#> 2023-05-08 20:11:43.969756 INFO::Fitting model to feature number 1284, ASV_2835
#> 2023-05-08 20:11:43.972857 INFO::Fitting model to feature number 1285, ASV_1889
#> 2023-05-08 20:11:43.976054 INFO::Fitting model to feature number 1286, ASV_54
#> 2023-05-08 20:11:43.979131 INFO::Fitting model to feature number 1287, ASV_130
#> 2023-05-08 20:11:43.982403 INFO::Fitting model to feature number 1288, ASV_2171
#> 2023-05-08 20:11:43.985568 INFO::Fitting model to feature number 1289, ASV_866
#> 2023-05-08 20:11:43.988655 INFO::Fitting model to feature number 1290, ASV_980
#> 2023-05-08 20:11:43.991681 INFO::Fitting model to feature number 1291, ASV_84
#> 2023-05-08 20:11:43.994696 INFO::Fitting model to feature number 1292, ASV_88
#> 2023-05-08 20:11:43.998701 INFO::Fitting model to feature number 1293, ASV_433
#> 2023-05-08 20:11:44.002695 INFO::Fitting model to feature number 1294, ASV_91
#> 2023-05-08 20:11:44.0059 INFO::Fitting model to feature number 1295, ASV_444
#> 2023-05-08 20:11:44.009685 INFO::Fitting model to feature number 1296, ASV_533
#> 2023-05-08 20:11:44.012904 INFO::Fitting model to feature number 1297, ASV_275
#> 2023-05-08 20:11:44.017598 INFO::Fitting model to feature number 1298, ASV_384
#> 2023-05-08 20:11:44.020891 INFO::Fitting model to feature number 1299, ASV_558
#> 2023-05-08 20:11:44.024727 INFO::Fitting model to feature number 1300, ASV_2233
#> 2023-05-08 20:11:44.027915 INFO::Fitting model to feature number 1301, ASV_496
#> 2023-05-08 20:11:44.031218 INFO::Fitting model to feature number 1302, ASV_2888
#> 2023-05-08 20:11:44.034413 INFO::Fitting model to feature number 1303, ASV_630
#> 2023-05-08 20:11:44.037457 INFO::Fitting model to feature number 1304, ASV_766
#> 2023-05-08 20:11:44.040477 INFO::Fitting model to feature number 1305, ASV_1308
#> 2023-05-08 20:11:44.043617 INFO::Fitting model to feature number 1306, ASV_2386
#> 2023-05-08 20:11:44.04681 INFO::Fitting model to feature number 1307, ASV_2718
#> 2023-05-08 20:11:44.049872 INFO::Fitting model to feature number 1308, ASV_183
#> 2023-05-08 20:11:44.052857 INFO::Fitting model to feature number 1309, ASV_254
#> 2023-05-08 20:11:44.055837 INFO::Fitting model to feature number 1310, ASV_1055
#> 2023-05-08 20:11:44.058864 INFO::Fitting model to feature number 1311, ASV_1847
#> 2023-05-08 20:11:44.062353 INFO::Fitting model to feature number 1312, ASV_1282
#> 2023-05-08 20:11:44.065508 INFO::Fitting model to feature number 1313, ASV_2879
#> 2023-05-08 20:11:44.068845 INFO::Fitting model to feature number 1314, ASV_64
#> 2023-05-08 20:11:44.0723 INFO::Fitting model to feature number 1315, ASV_428
#> 2023-05-08 20:11:44.076268 INFO::Fitting model to feature number 1316, ASV_854
#> 2023-05-08 20:11:44.079784 INFO::Fitting model to feature number 1317, ASV_1647
#> 2023-05-08 20:11:44.082991 INFO::Fitting model to feature number 1318, ASV_124
#> 2023-05-08 20:11:44.086421 INFO::Fitting model to feature number 1319, ASV_337
#> 2023-05-08 20:11:44.089581 INFO::Fitting model to feature number 1320, ASV_957
#> 2023-05-08 20:11:44.093254 INFO::Fitting model to feature number 1321, ASV_229
#> 2023-05-08 20:11:44.096604 INFO::Fitting model to feature number 1322, ASV_735
#> 2023-05-08 20:11:44.099854 INFO::Fitting model to feature number 1323, ASV_1407
#> 2023-05-08 20:11:44.103079 INFO::Fitting model to feature number 1324, ASV_2638
#> 2023-05-08 20:11:44.106131 INFO::Fitting model to feature number 1325, ASV_482
#> 2023-05-08 20:11:44.109298 INFO::Fitting model to feature number 1326, ASV_493
#> 2023-05-08 20:11:44.112418 INFO::Fitting model to feature number 1327, ASV_343
#> 2023-05-08 20:11:44.115617 INFO::Fitting model to feature number 1328, ASV_374
#> 2023-05-08 20:11:44.118733 INFO::Fitting model to feature number 1329, ASV_2643
#> 2023-05-08 20:11:44.122015 INFO::Fitting model to feature number 1330, ASV_250
#> 2023-05-08 20:11:44.125267 INFO::Fitting model to feature number 1331, ASV_1154
#> 2023-05-08 20:11:44.128361 INFO::Fitting model to feature number 1332, ASV_1196
#> 2023-05-08 20:11:44.131477 INFO::Fitting model to feature number 1333, ASV_2299
#> 2023-05-08 20:11:44.134762 INFO::Fitting model to feature number 1334, ASV_2116
#> 2023-05-08 20:11:44.137931 INFO::Fitting model to feature number 1335, ASV_1337
#> 2023-05-08 20:11:44.140999 INFO::Fitting model to feature number 1336, ASV_146
#> 2023-05-08 20:11:44.144016 INFO::Fitting model to feature number 1337, ASV_1419
#> 2023-05-08 20:11:44.147423 INFO::Fitting model to feature number 1338, ASV_1361
#> 2023-05-08 20:11:44.151073 INFO::Fitting model to feature number 1339, ASV_1648
#> 2023-05-08 20:11:44.154654 INFO::Fitting model to feature number 1340, ASV_1432
#> 2023-05-08 20:11:44.157876 INFO::Fitting model to feature number 1341, ASV_483
#> 2023-05-08 20:11:44.161187 INFO::Fitting model to feature number 1342, ASV_2556
#> 2023-05-08 20:11:44.164383 INFO::Fitting model to feature number 1343, ASV_1258
#> 2023-05-08 20:11:44.167486 INFO::Fitting model to feature number 1344, ASV_1444
#> 2023-05-08 20:11:44.170966 INFO::Fitting model to feature number 1345, ASV_2122
#> 2023-05-08 20:11:44.174495 INFO::Fitting model to feature number 1346, ASV_63
#> 2023-05-08 20:11:44.178177 INFO::Fitting model to feature number 1347, ASV_147
#> 2023-05-08 20:11:44.181708 INFO::Fitting model to feature number 1348, ASV_189
#> 2023-05-08 20:11:44.185125 INFO::Fitting model to feature number 1349, ASV_500
#> 2023-05-08 20:11:44.188296 INFO::Fitting model to feature number 1350, ASV_1586
#> 2023-05-08 20:11:44.191624 INFO::Fitting model to feature number 1351, ASV_2443
#> 2023-05-08 20:11:44.194865 INFO::Fitting model to feature number 1352, ASV_2424
#> 2023-05-08 20:11:44.19795 INFO::Fitting model to feature number 1353, ASV_1324
#> 2023-05-08 20:11:44.201043 INFO::Fitting model to feature number 1354, ASV_2003
#> 2023-05-08 20:11:44.204101 INFO::Fitting model to feature number 1355, ASV_1751
#> 2023-05-08 20:11:44.207268 INFO::Fitting model to feature number 1356, ASV_1829
#> 2023-05-08 20:11:44.210328 INFO::Fitting model to feature number 1357, ASV_199
#> 2023-05-08 20:11:44.213386 INFO::Fitting model to feature number 1358, ASV_659
#> 2023-05-08 20:11:44.216825 INFO::Fitting model to feature number 1359, ASV_1587
#> 2023-05-08 20:11:44.220077 INFO::Fitting model to feature number 1360, ASV_160
#> 2023-05-08 20:11:44.223557 INFO::Fitting model to feature number 1361, ASV_843
#> 2023-05-08 20:11:44.22682 INFO::Fitting model to feature number 1362, ASV_2000
#> 2023-05-08 20:11:44.230335 INFO::Fitting model to feature number 1363, ASV_268
#> 2023-05-08 20:11:44.233577 INFO::Fitting model to feature number 1364, ASV_750
#> 2023-05-08 20:11:44.23671 INFO::Fitting model to feature number 1365, ASV_1466
#> 2023-05-08 20:11:44.239767 INFO::Fitting model to feature number 1366, ASV_1881
#> 2023-05-08 20:11:44.242787 INFO::Fitting model to feature number 1367, ASV_312
#> 2023-05-08 20:11:44.2459 INFO::Fitting model to feature number 1368, ASV_2667
#> 2023-05-08 20:11:44.248913 INFO::Fitting model to feature number 1369, ASV_570
#> 2023-05-08 20:11:44.25193 INFO::Fitting model to feature number 1370, ASV_855
#> 2023-05-08 20:11:44.25529 INFO::Fitting model to feature number 1371, ASV_1628
#> 2023-05-08 20:11:44.258907 INFO::Fitting model to feature number 1372, ASV_1350
#> 2023-05-08 20:11:44.262338 INFO::Fitting model to feature number 1373, ASV_1067
#> 2023-05-08 20:11:44.2655 INFO::Fitting model to feature number 1374, ASV_1345
#> 2023-05-08 20:11:44.268526 INFO::Fitting model to feature number 1375, ASV_1469
#> 2023-05-08 20:11:44.271551 INFO::Fitting model to feature number 1376, ASV_2751
#> 2023-05-08 20:11:44.275363 INFO::Fitting model to feature number 1377, ASV_365
#> 2023-05-08 20:11:44.27924 INFO::Fitting model to feature number 1378, ASV_66
#> 2023-05-08 20:11:44.282396 INFO::Fitting model to feature number 1379, ASV_286
#> 2023-05-08 20:11:44.28556 INFO::Fitting model to feature number 1380, ASV_1323
#> 2023-05-08 20:11:44.28855 INFO::Fitting model to feature number 1381, ASV_260
#> 2023-05-08 20:11:44.291815 INFO::Fitting model to feature number 1382, ASV_503
#> 2023-05-08 20:11:44.294933 INFO::Fitting model to feature number 1383, ASV_1091
#> 2023-05-08 20:11:44.298298 INFO::Fitting model to feature number 1384, ASV_1433
#> 2023-05-08 20:11:44.301441 INFO::Fitting model to feature number 1385, ASV_2802
#> 2023-05-08 20:11:44.304584 INFO::Fitting model to feature number 1386, ASV_883
#> 2023-05-08 20:11:44.307932 INFO::Fitting model to feature number 1387, ASV_728
#> 2023-05-08 20:11:44.311237 INFO::Fitting model to feature number 1388, ASV_835
#> 2023-05-08 20:11:44.314659 INFO::Fitting model to feature number 1389, ASV_960
#> 2023-05-08 20:11:44.317864 INFO::Fitting model to feature number 1390, ASV_2712
#> 2023-05-08 20:11:44.320961 INFO::Fitting model to feature number 1391, ASV_1752
#> 2023-05-08 20:11:44.324701 INFO::Fitting model to feature number 1392, ASV_1486
#> 2023-05-08 20:11:44.328072 INFO::Fitting model to feature number 1393, ASV_616
#> 2023-05-08 20:11:44.331169 INFO::Fitting model to feature number 1394, ASV_1972
#> 2023-05-08 20:11:44.334175 INFO::Fitting model to feature number 1395, ASV_2708
#> 2023-05-08 20:11:44.337184 INFO::Fitting model to feature number 1396, ASV_1592
#> 2023-05-08 20:11:44.340735 INFO::Fitting model to feature number 1397, ASV_2758
#> 2023-05-08 20:11:44.344108 INFO::Fitting model to feature number 1398, ASV_1326
#> 2023-05-08 20:11:44.347541 INFO::Fitting model to feature number 1399, ASV_2378
#> 2023-05-08 20:11:44.351131 INFO::Fitting model to feature number 1400, ASV_1288
#> 2023-05-08 20:11:44.354599 INFO::Fitting model to feature number 1401, ASV_489
#> 2023-05-08 20:11:44.358223 INFO::Fitting model to feature number 1402, ASV_1649
#> 2023-05-08 20:11:44.361482 INFO::Fitting model to feature number 1403, ASV_2801
#> 2023-05-08 20:11:44.364569 INFO::Fitting model to feature number 1404, ASV_1968
#> 2023-05-08 20:11:44.367649 INFO::Fitting model to feature number 1405, ASV_803
#> 2023-05-08 20:11:44.371022 INFO::Fitting model to feature number 1406, ASV_2697
#> 2023-05-08 20:11:44.374601 INFO::Fitting model to feature number 1407, ASV_1599
#> 2023-05-08 20:11:44.377726 INFO::Fitting model to feature number 1408, ASV_2731
#> 2023-05-08 20:11:44.381009 INFO::Fitting model to feature number 1409, ASV_988
#> 2023-05-08 20:11:44.38426 INFO::Fitting model to feature number 1410, ASV_2579
#> 2023-05-08 20:11:44.388057 INFO::Fitting model to feature number 1411, ASV_2868
#> 2023-05-08 20:11:44.392253 INFO::Fitting model to feature number 1412, ASV_1583
#> 2023-05-08 20:11:44.395674 INFO::Fitting model to feature number 1413, ASV_1749
#> 2023-05-08 20:11:44.399157 INFO::Fitting model to feature number 1414, ASV_2479
#> 2023-05-08 20:11:44.402714 INFO::Fitting model to feature number 1415, ASV_2664
#> 2023-05-08 20:11:44.405835 INFO::Fitting model to feature number 1416, ASV_2012
#> 2023-05-08 20:11:44.409034 INFO::Fitting model to feature number 1417, ASV_647
#> 2023-05-08 20:11:44.412333 INFO::Fitting model to feature number 1418, ASV_1705
#> 2023-05-08 20:11:44.415472 INFO::Fitting model to feature number 1419, ASV_2255
#> 2023-05-08 20:11:44.4188 INFO::Fitting model to feature number 1420, ASV_2331
#> 2023-05-08 20:11:44.421899 INFO::Fitting model to feature number 1421, ASV_2749
#> 2023-05-08 20:11:44.424944 INFO::Fitting model to feature number 1422, ASV_2847
#> 2023-05-08 20:11:44.427903 INFO::Fitting model to feature number 1423, ASV_1162
#> 2023-05-08 20:11:44.430974 INFO::Fitting model to feature number 1424, ASV_1482
#> 2023-05-08 20:11:44.434508 INFO::Fitting model to feature number 1425, ASV_2493
#> 2023-05-08 20:11:44.438071 INFO::Fitting model to feature number 1426, ASV_2179
#> 2023-05-08 20:11:44.441257 INFO::Fitting model to feature number 1427, ASV_67
#> 2023-05-08 20:11:44.444331 INFO::Fitting model to feature number 1428, ASV_202
#> 2023-05-08 20:11:44.447856 INFO::Fitting model to feature number 1429, ASV_310
#> 2023-05-08 20:11:44.451914 INFO::Fitting model to feature number 1430, ASV_1995
#> 2023-05-08 20:11:44.455341 INFO::Fitting model to feature number 1431, ASV_575
#> 2023-05-08 20:11:44.458556 INFO::Fitting model to feature number 1432, ASV_2232
#> 2023-05-08 20:11:44.461746 INFO::Fitting model to feature number 1433, ASV_90
#> 2023-05-08 20:11:44.465329 INFO::Fitting model to feature number 1434, ASV_1045
#> 2023-05-08 20:11:44.468682 INFO::Fitting model to feature number 1435, ASV_1202
#> 2023-05-08 20:11:44.472005 INFO::Fitting model to feature number 1436, ASV_2157
#> 2023-05-08 20:11:44.475394 INFO::Fitting model to feature number 1437, ASV_531
#> 2023-05-08 20:11:44.478609 INFO::Fitting model to feature number 1438, ASV_661
#> 2023-05-08 20:11:44.482049 INFO::Fitting model to feature number 1439, ASV_217
#> 2023-05-08 20:11:44.485476 INFO::Fitting model to feature number 1440, ASV_403
#> 2023-05-08 20:11:44.488754 INFO::Fitting model to feature number 1441, ASV_1105
#> 2023-05-08 20:11:44.491947 INFO::Fitting model to feature number 1442, ASV_2365
#> 2023-05-08 20:11:44.49522 INFO::Fitting model to feature number 1443, ASV_2769
#> 2023-05-08 20:11:44.498557 INFO::Fitting model to feature number 1444, ASV_333
#> 2023-05-08 20:11:44.501751 INFO::Fitting model to feature number 1445, ASV_1979
#> 2023-05-08 20:11:44.504857 INFO::Fitting model to feature number 1446, ASV_903
#> 2023-05-08 20:11:44.50801 INFO::Fitting model to feature number 1447, ASV_143
#> 2023-05-08 20:11:44.51127 INFO::Fitting model to feature number 1448, ASV_375
#> 2023-05-08 20:11:44.515063 INFO::Fitting model to feature number 1449, ASV_399
#> 2023-05-08 20:11:44.518324 INFO::Fitting model to feature number 1450, ASV_1262
#> 2023-05-08 20:11:44.521797 INFO::Fitting model to feature number 1451, ASV_70
#> 2023-05-08 20:11:44.524886 INFO::Fitting model to feature number 1452, ASV_80
#> 2023-05-08 20:11:44.528429 INFO::Fitting model to feature number 1453, ASV_445
#> 2023-05-08 20:11:44.531927 INFO::Fitting model to feature number 1454, ASV_76
#> 2023-05-08 20:11:44.535097 INFO::Fitting model to feature number 1455, ASV_233
#> 2023-05-08 20:11:44.53817 INFO::Fitting model to feature number 1456, ASV_133
#> 2023-05-08 20:11:44.541318 INFO::Fitting model to feature number 1457, ASV_242
#> 2023-05-08 20:11:44.544688 INFO::Fitting model to feature number 1458, ASV_485
#> 2023-05-08 20:11:44.547978 INFO::Fitting model to feature number 1459, ASV_827
#> 2023-05-08 20:11:44.551416 INFO::Fitting model to feature number 1460, ASV_1339
#> 2023-05-08 20:11:44.554614 INFO::Fitting model to feature number 1461, ASV_93
#> 2023-05-08 20:11:44.558197 INFO::Fitting model to feature number 1462, ASV_946
#> 2023-05-08 20:11:44.56181 INFO::Fitting model to feature number 1463, ASV_1066
#> 2023-05-08 20:11:44.565055 INFO::Fitting model to feature number 1464, ASV_2259
#> 2023-05-08 20:11:44.56814 INFO::Fitting model to feature number 1465, ASV_252
#> 2023-05-08 20:11:44.571156 INFO::Fitting model to feature number 1466, ASV_450
#> 2023-05-08 20:11:44.574302 INFO::Fitting model to feature number 1467, ASV_820
#> 2023-05-08 20:11:44.577698 INFO::Fitting model to feature number 1468, ASV_2427
#> 2023-05-08 20:11:44.580787 INFO::Fitting model to feature number 1469, ASV_393
#> 2023-05-08 20:11:44.584311 INFO::Fitting model to feature number 1470, ASV_933
#> 2023-05-08 20:11:44.587452 INFO::Fitting model to feature number 1471, ASV_1981
#> 2023-05-08 20:11:44.590792 INFO::Fitting model to feature number 1472, ASV_501
#> 2023-05-08 20:11:44.594031 INFO::Fitting model to feature number 1473, ASV_508
#> 2023-05-08 20:11:44.597406 INFO::Fitting model to feature number 1474, ASV_853
#> 2023-05-08 20:11:44.600924 INFO::Fitting model to feature number 1475, ASV_2134
#> 2023-05-08 20:11:44.604638 INFO::Fitting model to feature number 1476, ASV_2875
#> 2023-05-08 20:11:44.608179 INFO::Fitting model to feature number 1477, ASV_1049
#> 2023-05-08 20:11:44.611376 INFO::Fitting model to feature number 1478, ASV_2355
#> 2023-05-08 20:11:44.614434 INFO::Fitting model to feature number 1479, ASV_1720
#> 2023-05-08 20:11:44.617447 INFO::Fitting model to feature number 1480, ASV_184
#> 2023-05-08 20:11:44.620611 INFO::Fitting model to feature number 1481, ASV_2447
#> 2023-05-08 20:11:44.623795 INFO::Fitting model to feature number 1482, ASV_436
#> 2023-05-08 20:11:44.627337 INFO::Fitting model to feature number 1483, ASV_758
#> 2023-05-08 20:11:44.630447 INFO::Fitting model to feature number 1484, ASV_1631
#> 2023-05-08 20:11:44.634109 INFO::Fitting model to feature number 1485, ASV_2044
#> 2023-05-08 20:11:44.637691 INFO::Fitting model to feature number 1486, ASV_344
#> 2023-05-08 20:11:44.640903 INFO::Fitting model to feature number 1487, ASV_1355
#> 2023-05-08 20:11:44.643945 INFO::Fitting model to feature number 1488, ASV_426
#> 2023-05-08 20:11:44.64694 INFO::Fitting model to feature number 1489, ASV_361
#> 2023-05-08 20:11:44.649927 INFO::Fitting model to feature number 1490, ASV_1530
#> 2023-05-08 20:11:44.653326 INFO::Fitting model to feature number 1491, ASV_291
#> 2023-05-08 20:11:44.656638 INFO::Fitting model to feature number 1492, ASV_1689
#> 2023-05-08 20:11:44.659782 INFO::Fitting model to feature number 1493, ASV_774
#> 2023-05-08 20:11:44.662814 INFO::Fitting model to feature number 1494, ASV_2004
#> 2023-05-08 20:11:44.665828 INFO::Fitting model to feature number 1495, ASV_2339
#> 2023-05-08 20:11:44.669006 INFO::Fitting model to feature number 1496, ASV_841
#> 2023-05-08 20:11:44.672388 INFO::Fitting model to feature number 1497, ASV_1204
#> 2023-05-08 20:11:44.675967 INFO::Fitting model to feature number 1498, ASV_1862
#> 2023-05-08 20:11:44.679225 INFO::Fitting model to feature number 1499, ASV_363
#> 2023-05-08 20:11:44.682302 INFO::Fitting model to feature number 1500, ASV_1426
#> 2023-05-08 20:11:44.685567 INFO::Fitting model to feature number 1501, ASV_676
#> 2023-05-08 20:11:44.688655 INFO::Fitting model to feature number 1502, ASV_2744
#> 2023-05-08 20:11:44.691855 INFO::Fitting model to feature number 1503, ASV_828
#> 2023-05-08 20:11:44.694919 INFO::Fitting model to feature number 1504, ASV_1600
#> 2023-05-08 20:11:44.697986 INFO::Fitting model to feature number 1505, ASV_2694
#> 2023-05-08 20:11:44.701867 INFO::Fitting model to feature number 1506, ASV_905
#> 2023-05-08 20:11:44.705052 INFO::Fitting model to feature number 1507, ASV_2087
#> 2023-05-08 20:11:44.708118 INFO::Fitting model to feature number 1508, ASV_2456
#> 2023-05-08 20:11:44.71116 INFO::Fitting model to feature number 1509, ASV_2451
#> 2023-05-08 20:11:44.714362 INFO::Fitting model to feature number 1510, ASV_2767
#> 2023-05-08 20:11:44.717795 INFO::Fitting model to feature number 1511, ASV_371
#> 2023-05-08 20:11:44.721287 INFO::Fitting model to feature number 1512, ASV_687
#> 2023-05-08 20:11:44.724714 INFO::Fitting model to feature number 1513, ASV_419
#> 2023-05-08 20:11:44.728129 INFO::Fitting model to feature number 1514, ASV_411
#> 2023-05-08 20:11:44.731893 INFO::Fitting model to feature number 1515, ASV_1207
#> 2023-05-08 20:11:44.735347 INFO::Fitting model to feature number 1516, ASV_1679
#> 2023-05-08 20:11:44.738616 INFO::Fitting model to feature number 1517, ASV_2263
#> 2023-05-08 20:11:44.741833 INFO::Fitting model to feature number 1518, ASV_2690
#> 2023-05-08 20:11:44.745002 INFO::Fitting model to feature number 1519, ASV_1044
#> 2023-05-08 20:11:44.74833 INFO::Fitting model to feature number 1520, ASV_1157
#> 2023-05-08 20:11:44.751465 INFO::Fitting model to feature number 1521, ASV_1598
#> 2023-05-08 20:11:44.754487 INFO::Fitting model to feature number 1522, ASV_2185
#> 2023-05-08 20:11:44.757445 INFO::Fitting model to feature number 1523, ASV_1735
#> 2023-05-08 20:11:44.760532 INFO::Fitting model to feature number 1524, ASV_1074
#> 2023-05-08 20:11:44.764006 INFO::Fitting model to feature number 1525, ASV_1688
#> 2023-05-08 20:11:44.767157 INFO::Fitting model to feature number 1526, ASV_2550
#> 2023-05-08 20:11:44.77044 INFO::Fitting model to feature number 1527, ASV_1310
#> 2023-05-08 20:11:44.773733 INFO::Fitting model to feature number 1528, ASV_1478
#> 2023-05-08 20:11:44.7771 INFO::Fitting model to feature number 1529, ASV_1942
#> 2023-05-08 20:11:44.780491 INFO::Fitting model to feature number 1530, ASV_2120
#> 2023-05-08 20:11:44.783865 INFO::Fitting model to feature number 1531, ASV_2586
#> 2023-05-08 20:11:44.787091 INFO::Fitting model to feature number 1532, ASV_2222
#> 2023-05-08 20:11:44.790202 INFO::Fitting model to feature number 1533, ASV_2117
#> 2023-05-08 20:11:44.793611 INFO::Fitting model to feature number 1534, ASV_2370
#> 2023-05-08 20:11:44.796928 INFO::Fitting model to feature number 1535, ASV_2309
#> 2023-05-08 20:11:44.800119 INFO::Fitting model to feature number 1536, ASV_81
#> 2023-05-08 20:11:44.803183 INFO::Fitting model to feature number 1537, ASV_392
#> 2023-05-08 20:11:44.806252 INFO::Fitting model to feature number 1538, ASV_2336
#> 2023-05-08 20:11:44.809286 INFO::Fitting model to feature number 1539, ASV_899
#> 2023-05-08 20:11:44.812309 INFO::Fitting model to feature number 1540, ASV_1378
#> 2023-05-08 20:11:44.815346 INFO::Fitting model to feature number 1541, ASV_2402
#> 2023-05-08 20:11:44.818405 INFO::Fitting model to feature number 1542, ASV_1454
#> 2023-05-08 20:11:44.821593 INFO::Fitting model to feature number 1543, ASV_2674
#> 2023-05-08 20:11:44.825499 INFO::Fitting model to feature number 1544, ASV_114
#> 2023-05-08 20:11:44.828898 INFO::Fitting model to feature number 1545, ASV_1900
#> 2023-05-08 20:11:44.832178 INFO::Fitting model to feature number 1546, ASV_1791
#> 2023-05-08 20:11:44.83562 INFO::Fitting model to feature number 1547, ASV_2503
#> 2023-05-08 20:11:44.839066 INFO::Fitting model to feature number 1548, ASV_1112
#> 2023-05-08 20:11:44.84316 INFO::Fitting model to feature number 1549, ASV_2284
#> 2023-05-08 20:11:44.8464 INFO::Fitting model to feature number 1550, ASV_1403
#> 2023-05-08 20:11:44.849853 INFO::Fitting model to feature number 1551, ASV_529
#> 2023-05-08 20:11:44.852963 INFO::Fitting model to feature number 1552, ASV_893
#> 2023-05-08 20:11:44.856199 INFO::Fitting model to feature number 1553, ASV_2071
#> 2023-05-08 20:11:44.859249 INFO::Fitting model to feature number 1554, ASV_976
#> 2023-05-08 20:11:44.862265 INFO::Fitting model to feature number 1555, ASV_1866
#> 2023-05-08 20:11:44.865255 INFO::Fitting model to feature number 1556, ASV_1408
#> 2023-05-08 20:11:44.868543 INFO::Fitting model to feature number 1557, ASV_1620
#> 2023-05-08 20:11:44.871622 INFO::Fitting model to feature number 1558, ASV_1946
#> 2023-05-08 20:11:44.875234 INFO::Fitting model to feature number 1559, ASV_1959
#> 2023-05-08 20:11:44.878553 INFO::Fitting model to feature number 1560, ASV_2036
#> 2023-05-08 20:11:44.882102 INFO::Fitting model to feature number 1561, ASV_101
#> 2023-05-08 20:11:44.885401 INFO::Fitting model to feature number 1562, ASV_1460
#> 2023-05-08 20:11:44.888783 INFO::Fitting model to feature number 1563, ASV_429
#> 2023-05-08 20:11:44.892017 INFO::Fitting model to feature number 1564, ASV_579
#> 2023-05-08 20:11:44.89529 INFO::Fitting model to feature number 1565, ASV_488
#> 2023-05-08 20:11:44.89843 INFO::Fitting model to feature number 1566, ASV_1859
#> 2023-05-08 20:11:44.901531 INFO::Fitting model to feature number 1567, ASV_373
#> 2023-05-08 20:11:44.904799 INFO::Fitting model to feature number 1568, ASV_1115
#> 2023-05-08 20:11:44.908684 INFO::Fitting model to feature number 1569, ASV_1133
#> 2023-05-08 20:11:44.911957 INFO::Fitting model to feature number 1570, ASV_2418
#> 2023-05-08 20:11:44.914983 INFO::Fitting model to feature number 1571, ASV_773
#> 2023-05-08 20:11:44.917988 INFO::Fitting model to feature number 1572, ASV_1059
#> 2023-05-08 20:11:44.921321 INFO::Fitting model to feature number 1573, ASV_210
#> 2023-05-08 20:11:44.925217 INFO::Fitting model to feature number 1574, ASV_1510
#> 2023-05-08 20:11:44.928419 INFO::Fitting model to feature number 1575, ASV_1813
#> 2023-05-08 20:11:44.931493 INFO::Fitting model to feature number 1576, ASV_1459
#> 2023-05-08 20:11:44.934544 INFO::Fitting model to feature number 1577, ASV_604
#> 2023-05-08 20:11:44.937785 INFO::Fitting model to feature number 1578, ASV_1216
#> 2023-05-08 20:11:44.940929 INFO::Fitting model to feature number 1579, ASV_110
#> 2023-05-08 20:11:44.944249 INFO::Fitting model to feature number 1580, ASV_1781
#> 2023-05-08 20:11:44.947777 INFO::Fitting model to feature number 1581, ASV_1015
#> 2023-05-08 20:11:44.95088 INFO::Fitting model to feature number 1582, ASV_1937
#> 2023-05-08 20:11:44.954101 INFO::Fitting model to feature number 1583, ASV_2609
#> 2023-05-08 20:11:44.957167 INFO::Fitting model to feature number 1584, ASV_643
#> 2023-05-08 20:11:44.960178 INFO::Fitting model to feature number 1585, ASV_1346
#> 2023-05-08 20:11:44.963174 INFO::Fitting model to feature number 1586, ASV_1733
#> 2023-05-08 20:11:44.966579 INFO::Fitting model to feature number 1587, ASV_2246
#> 2023-05-08 20:11:44.969929 INFO::Fitting model to feature number 1588, ASV_2040
#> 2023-05-08 20:11:44.973174 INFO::Fitting model to feature number 1589, ASV_1102
#> 2023-05-08 20:11:44.976622 INFO::Fitting model to feature number 1590, ASV_2577
#> 2023-05-08 20:11:44.979777 INFO::Fitting model to feature number 1591, ASV_112
#> 2023-05-08 20:11:44.982824 INFO::Fitting model to feature number 1592, ASV_1200
#> 2023-05-08 20:11:44.999897 INFO::Fitting model to feature number 1593, ASV_2008
#> 2023-05-08 20:11:45.004548 INFO::Fitting model to feature number 1594, ASV_2366
#> 2023-05-08 20:11:45.008505 INFO::Fitting model to feature number 1595, ASV_247
#> 2023-05-08 20:11:45.012037 INFO::Fitting model to feature number 1596, ASV_1446
#> 2023-05-08 20:11:45.015534 INFO::Fitting model to feature number 1597, ASV_1504
#> 2023-05-08 20:11:45.019176 INFO::Fitting model to feature number 1598, ASV_2385
#> 2023-05-08 20:11:45.022537 INFO::Fitting model to feature number 1599, ASV_1506
#> 2023-05-08 20:11:45.026236 INFO::Fitting model to feature number 1600, ASV_2821
#> 2023-05-08 20:11:45.029654 INFO::Fitting model to feature number 1601, ASV_1597
#> 2023-05-08 20:11:45.03307 INFO::Fitting model to feature number 1602, ASV_2444
#> 2023-05-08 20:11:45.036389 INFO::Fitting model to feature number 1603, ASV_2364
#> 2023-05-08 20:11:45.039607 INFO::Fitting model to feature number 1604, ASV_1660
#> 2023-05-08 20:11:45.042763 INFO::Fitting model to feature number 1605, ASV_970
#> 2023-05-08 20:11:45.045921 INFO::Fitting model to feature number 1606, ASV_2433
#> 2023-05-08 20:11:45.049438 INFO::Fitting model to feature number 1607, ASV_2241
#> 2023-05-08 20:11:45.052704 INFO::Fitting model to feature number 1608, ASV_2358
#> 2023-05-08 20:11:45.056102 INFO::Fitting model to feature number 1609, ASV_121
#> 2023-05-08 20:11:45.059809 INFO::Fitting model to feature number 1610, ASV_1970
#> 2023-05-08 20:11:45.063489 INFO::Fitting model to feature number 1611, ASV_2762
#> 2023-05-08 20:11:45.066893 INFO::Fitting model to feature number 1612, ASV_370
#> 2023-05-08 20:11:45.070115 INFO::Fitting model to feature number 1613, ASV_1333
#> 2023-05-08 20:11:45.073231 INFO::Fitting model to feature number 1614, ASV_2142
#> 2023-05-08 20:11:45.076554 INFO::Fitting model to feature number 1615, ASV_1265
#> 2023-05-08 20:11:45.079979 INFO::Fitting model to feature number 1616, ASV_2448
#> 2023-05-08 20:11:45.083201 INFO::Fitting model to feature number 1617, ASV_463
#> 2023-05-08 20:11:45.086331 INFO::Fitting model to feature number 1618, ASV_936
#> 2023-05-08 20:11:45.089424 INFO::Fitting model to feature number 1619, ASV_792
#> 2023-05-08 20:11:45.092789 INFO::Fitting model to feature number 1620, ASV_1381
#> 2023-05-08 20:11:45.096461 INFO::Fitting model to feature number 1621, ASV_2512
#> 2023-05-08 20:11:45.099658 INFO::Fitting model to feature number 1622, ASV_2015
#> 2023-05-08 20:11:45.10269 INFO::Fitting model to feature number 1623, ASV_2021
#> 2023-05-08 20:11:45.105971 INFO::Fitting model to feature number 1624, ASV_2329
#> 2023-05-08 20:11:45.109326 INFO::Fitting model to feature number 1625, ASV_2219
#> 2023-05-08 20:11:45.112621 INFO::Fitting model to feature number 1626, ASV_632
#> 2023-05-08 20:11:45.115841 INFO::Fitting model to feature number 1627, ASV_1438
#> 2023-05-08 20:11:45.118963 INFO::Fitting model to feature number 1628, ASV_2714
#> 2023-05-08 20:11:45.122049 INFO::Fitting model to feature number 1629, ASV_800
#> 2023-05-08 20:11:45.125338 INFO::Fitting model to feature number 1630, ASV_1456
#> 2023-05-08 20:11:45.128818 INFO::Fitting model to feature number 1631, ASV_2496
#> 2023-05-08 20:11:45.132132 INFO::Fitting model to feature number 1632, ASV_1807
#> 2023-05-08 20:11:45.135523 INFO::Fitting model to feature number 1633, ASV_1895
#> 2023-05-08 20:11:45.13882 INFO::Fitting model to feature number 1634, ASV_2356
#> 2023-05-08 20:11:45.142078 INFO::Fitting model to feature number 1635, ASV_122
#> 2023-05-08 20:11:45.145403 INFO::Fitting model to feature number 1636, ASV_699
#> 2023-05-08 20:11:45.148494 INFO::Fitting model to feature number 1637, ASV_1614
#> 2023-05-08 20:11:45.151735 INFO::Fitting model to feature number 1638, ASV_2316
#> 2023-05-08 20:11:45.154876 INFO::Fitting model to feature number 1639, ASV_163
#> 2023-05-08 20:11:45.158477 INFO::Fitting model to feature number 1640, ASV_381
#> 2023-05-08 20:11:45.161767 INFO::Fitting model to feature number 1641, ASV_1637
#> 2023-05-08 20:11:45.165135 INFO::Fitting model to feature number 1642, ASV_326
#> 2023-05-08 20:11:45.168289 INFO::Fitting model to feature number 1643, ASV_1335
#> 2023-05-08 20:11:45.171531 INFO::Fitting model to feature number 1644, ASV_2067
#> 2023-05-08 20:11:45.174894 INFO::Fitting model to feature number 1645, ASV_1555
#> 2023-05-08 20:11:45.17817 INFO::Fitting model to feature number 1646, ASV_2016
#> 2023-05-08 20:11:45.18143 INFO::Fitting model to feature number 1647, ASV_2057
#> 2023-05-08 20:11:45.184653 INFO::Fitting model to feature number 1648, ASV_180
#> 2023-05-08 20:11:45.188044 INFO::Fitting model to feature number 1649, ASV_352
#> 2023-05-08 20:11:45.191135 INFO::Fitting model to feature number 1650, ASV_584
#> 2023-05-08 20:11:45.194163 INFO::Fitting model to feature number 1651, ASV_1470
#> 2023-05-08 20:11:45.197156 INFO::Fitting model to feature number 1652, ASV_1605
#> 2023-05-08 20:11:45.200305 INFO::Fitting model to feature number 1653, ASV_2478
#> 2023-05-08 20:11:45.203616 INFO::Fitting model to feature number 1654, ASV_2701
#> 2023-05-08 20:11:45.207224 INFO::Fitting model to feature number 1655, ASV_2878
#> 2023-05-08 20:11:45.210517 INFO::Fitting model to feature number 1656, ASV_151
#> 2023-05-08 20:11:45.213639 INFO::Fitting model to feature number 1657, ASV_2130
#> 2023-05-08 20:11:45.216715 INFO::Fitting model to feature number 1658, ASV_308
#> 2023-05-08 20:11:45.220068 INFO::Fitting model to feature number 1659, ASV_842
#> 2023-05-08 20:11:45.223549 INFO::Fitting model to feature number 1660, ASV_1224
#> 2023-05-08 20:11:45.226985 INFO::Fitting model to feature number 1661, ASV_825
#> 2023-05-08 20:11:45.230162 INFO::Fitting model to feature number 1662, ASV_2286
#> 2023-05-08 20:11:45.233271 INFO::Fitting model to feature number 1663, ASV_1710
#> 2023-05-08 20:11:45.236556 INFO::Fitting model to feature number 1664, ASV_2023
#> 2023-05-08 20:11:45.239852 INFO::Fitting model to feature number 1665, ASV_1138
#> 2023-05-08 20:11:45.243204 INFO::Fitting model to feature number 1666, ASV_1434
#> 2023-05-08 20:11:45.246464 INFO::Fitting model to feature number 1667, ASV_1800
#> 2023-05-08 20:11:45.249578 INFO::Fitting model to feature number 1668, ASV_1213
#> 2023-05-08 20:11:45.252985 INFO::Fitting model to feature number 1669, ASV_492
#> 2023-05-08 20:11:45.256333 INFO::Fitting model to feature number 1670, ASV_1774
#> 2023-05-08 20:11:45.259443 INFO::Fitting model to feature number 1671, ASV_1696
#> 2023-05-08 20:11:45.262497 INFO::Fitting model to feature number 1672, ASV_635
#> 2023-05-08 20:11:45.265793 INFO::Fitting model to feature number 1673, ASV_737
#> 2023-05-08 20:11:45.269171 INFO::Fitting model to feature number 1674, ASV_943
#> 2023-05-08 20:11:45.27238 INFO::Fitting model to feature number 1675, ASV_2020
#> 2023-05-08 20:11:45.275445 INFO::Fitting model to feature number 1676, ASV_1639
#> 2023-05-08 20:11:45.278448 INFO::Fitting model to feature number 1677, ASV_2063
#> 2023-05-08 20:11:45.28147 INFO::Fitting model to feature number 1678, ASV_879
#> 2023-05-08 20:11:45.285418 INFO::Fitting model to feature number 1679, ASV_2078
#> 2023-05-08 20:11:45.289033 INFO::Fitting model to feature number 1680, ASV_1338
#> 2023-05-08 20:11:45.29242 INFO::Fitting model to feature number 1681, ASV_1928
#> 2023-05-08 20:11:45.295633 INFO::Fitting model to feature number 1682, ASV_524
#> 2023-05-08 20:11:45.299149 INFO::Fitting model to feature number 1683, ASV_2390
#> 2023-05-08 20:11:45.302473 INFO::Fitting model to feature number 1684, ASV_2591
#> 2023-05-08 20:11:45.305596 INFO::Fitting model to feature number 1685, ASV_1617
#> 2023-05-08 20:11:45.308844 INFO::Fitting model to feature number 1686, ASV_2704
#> 2023-05-08 20:11:45.311926 INFO::Fitting model to feature number 1687, ASV_2296
#> 2023-05-08 20:11:45.315033 INFO::Fitting model to feature number 1688, ASV_2613
#> 2023-05-08 20:11:45.318447 INFO::Fitting model to feature number 1689, ASV_332
#> 2023-05-08 20:11:45.322626 INFO::Fitting model to feature number 1690, ASV_2409
#> 2023-05-08 20:11:45.325914 INFO::Fitting model to feature number 1691, ASV_2492
#> 2023-05-08 20:11:45.329014 INFO::Fitting model to feature number 1692, ASV_2592
#> 2023-05-08 20:11:45.332933 INFO::Fitting model to feature number 1693, ASV_1283
#> 2023-05-08 20:11:45.336281 INFO::Fitting model to feature number 1694, ASV_200
#> 2023-05-08 20:11:45.340087 INFO::Fitting model to feature number 1695, ASV_876
#> 2023-05-08 20:11:45.343419 INFO::Fitting model to feature number 1696, ASV_2328
#> 2023-05-08 20:11:45.347233 INFO::Fitting model to feature number 1697, ASV_1989
#> 2023-05-08 20:11:45.350823 INFO::Fitting model to feature number 1698, ASV_865
#> 2023-05-08 20:11:45.354017 INFO::Fitting model to feature number 1699, ASV_875
#> 2023-05-08 20:11:45.357269 INFO::Fitting model to feature number 1700, ASV_2642
#> 2023-05-08 20:11:45.360533 INFO::Fitting model to feature number 1701, ASV_908
#> 2023-05-08 20:11:45.364124 INFO::Fitting model to feature number 1702, ASV_1739
#> 2023-05-08 20:11:45.367593 INFO::Fitting model to feature number 1703, ASV_2614
#> 2023-05-08 20:11:45.371092 INFO::Fitting model to feature number 1704, ASV_449
#> 2023-05-08 20:11:45.374558 INFO::Fitting model to feature number 1705, ASV_975
#> 2023-05-08 20:11:45.378124 INFO::Fitting model to feature number 1706, ASV_216
#> 2023-05-08 20:11:45.381494 INFO::Fitting model to feature number 1707, ASV_270
#> 2023-05-08 20:11:45.384858 INFO::Fitting model to feature number 1708, ASV_2381
#> 2023-05-08 20:11:45.388278 INFO::Fitting model to feature number 1709, ASV_218
#> 2023-05-08 20:11:45.392017 INFO::Fitting model to feature number 1710, ASV_2075
#> 2023-05-08 20:11:45.395348 INFO::Fitting model to feature number 1711, ASV_1467
#> 2023-05-08 20:11:45.398545 INFO::Fitting model to feature number 1712, ASV_1775
#> 2023-05-08 20:11:45.401613 INFO::Fitting model to feature number 1713, ASV_1782
#> 2023-05-08 20:11:45.404589 INFO::Fitting model to feature number 1714, ASV_2815
#> 2023-05-08 20:11:45.407753 INFO::Fitting model to feature number 1715, ASV_156
#> 2023-05-08 20:11:45.410887 INFO::Fitting model to feature number 1716, ASV_207
#> 2023-05-08 20:11:45.414108 INFO::Fitting model to feature number 1717, ASV_765
#> 2023-05-08 20:11:45.417204 INFO::Fitting model to feature number 1718, ASV_1307
#> 2023-05-08 20:11:45.420246 INFO::Fitting model to feature number 1719, ASV_2252
#> 2023-05-08 20:11:45.423487 INFO::Fitting model to feature number 1720, ASV_336
#> 2023-05-08 20:11:45.42692 INFO::Fitting model to feature number 1721, ASV_359
#> 2023-05-08 20:11:45.430096 INFO::Fitting model to feature number 1722, ASV_438
#> 2023-05-08 20:11:45.433319 INFO::Fitting model to feature number 1723, ASV_2691
#> 2023-05-08 20:11:45.436901 INFO::Fitting model to feature number 1724, ASV_2884
#> 2023-05-08 20:11:45.440278 INFO::Fitting model to feature number 1725, ASV_720
#> 2023-05-08 20:11:45.443538 INFO::Fitting model to feature number 1726, ASV_819
#> 2023-05-08 20:11:45.446687 INFO::Fitting model to feature number 1727, ASV_1321
#> 2023-05-08 20:11:45.449729 INFO::Fitting model to feature number 1728, ASV_349
#> 2023-05-08 20:11:45.452785 INFO::Fitting model to feature number 1729, ASV_789
#> 2023-05-08 20:11:45.456065 INFO::Fitting model to feature number 1730, ASV_1016
#> 2023-05-08 20:11:45.45956 INFO::Fitting model to feature number 1731, ASV_1314
#> 2023-05-08 20:11:45.462794 INFO::Fitting model to feature number 1732, ASV_1392
#> 2023-05-08 20:11:45.465798 INFO::Fitting model to feature number 1733, ASV_2306
#> 2023-05-08 20:11:45.468752 INFO::Fitting model to feature number 1734, ASV_937
#> 2023-05-08 20:11:45.472009 INFO::Fitting model to feature number 1735, ASV_1435
#> 2023-05-08 20:11:45.475072 INFO::Fitting model to feature number 1736, ASV_963
#> 2023-05-08 20:11:45.478075 INFO::Fitting model to feature number 1737, ASV_377
#> 2023-05-08 20:11:45.48122 INFO::Fitting model to feature number 1738, ASV_999
#> 2023-05-08 20:11:45.484421 INFO::Fitting model to feature number 1739, ASV_1706
#> 2023-05-08 20:11:45.487683 INFO::Fitting model to feature number 1740, ASV_2138
#> 2023-05-08 20:11:45.491395 INFO::Fitting model to feature number 1741, ASV_2315
#> 2023-05-08 20:11:45.494704 INFO::Fitting model to feature number 1742, ASV_382
#> 2023-05-08 20:11:45.497812 INFO::Fitting model to feature number 1743, ASV_474
#> 2023-05-08 20:11:45.500832 INFO::Fitting model to feature number 1744, ASV_387
#> 2023-05-08 20:11:45.504669 INFO::Fitting model to feature number 1745, ASV_549
#> 2023-05-08 20:11:45.50835 INFO::Fitting model to feature number 1746, ASV_576
#> 2023-05-08 20:11:45.511666 INFO::Fitting model to feature number 1747, ASV_712
#> 2023-05-08 20:11:45.514858 INFO::Fitting model to feature number 1748, ASV_1532
#> 2023-05-08 20:11:45.518425 INFO::Fitting model to feature number 1749, ASV_1685
#> 2023-05-08 20:11:45.52197 INFO::Fitting model to feature number 1750, ASV_1393
#> 2023-05-08 20:11:45.525392 INFO::Fitting model to feature number 1751, ASV_2307
#> 2023-05-08 20:11:45.528734 INFO::Fitting model to feature number 1752, ASV_821
#> 2023-05-08 20:11:45.53197 INFO::Fitting model to feature number 1753, ASV_1541
#> 2023-05-08 20:11:45.535187 INFO::Fitting model to feature number 1754, ASV_295
#> 2023-05-08 20:11:45.538381 INFO::Fitting model to feature number 1755, ASV_1239
#> 2023-05-08 20:11:45.541762 INFO::Fitting model to feature number 1756, ASV_1331
#> 2023-05-08 20:11:45.5449 INFO::Fitting model to feature number 1757, ASV_1927
#> 2023-05-08 20:11:45.54809 INFO::Fitting model to feature number 1758, ASV_1385
#> 2023-05-08 20:11:45.551406 INFO::Fitting model to feature number 1759, ASV_1999
#> 2023-05-08 20:11:45.554552 INFO::Fitting model to feature number 1760, ASV_342
#> 2023-05-08 20:11:45.557686 INFO::Fitting model to feature number 1761, ASV_2629
#> 2023-05-08 20:11:45.561085 INFO::Fitting model to feature number 1762, ASV_986
#> 2023-05-08 20:11:45.564217 INFO::Fitting model to feature number 1763, ASV_1377
#> 2023-05-08 20:11:45.56779 INFO::Fitting model to feature number 1764, ASV_1593
#> 2023-05-08 20:11:45.570839 INFO::Fitting model to feature number 1765, ASV_1584
#> 2023-05-08 20:11:45.573978 INFO::Fitting model to feature number 1766, ASV_2027
#> 2023-05-08 20:11:45.577051 INFO::Fitting model to feature number 1767, ASV_1998
#> 2023-05-08 20:11:45.580054 INFO::Fitting model to feature number 1768, ASV_161
#> 2023-05-08 20:11:45.58312 INFO::Fitting model to feature number 1769, ASV_1472
#> 2023-05-08 20:11:45.586205 INFO::Fitting model to feature number 1770, ASV_2167
#> 2023-05-08 20:11:45.589193 INFO::Fitting model to feature number 1771, ASV_223
#> 2023-05-08 20:11:45.592155 INFO::Fitting model to feature number 1772, ASV_633
#> 2023-05-08 20:11:45.595289 INFO::Fitting model to feature number 1773, ASV_771
#> 2023-05-08 20:11:45.598653 INFO::Fitting model to feature number 1774, ASV_1792
#> 2023-05-08 20:11:45.601851 INFO::Fitting model to feature number 1775, ASV_447
#> 2023-05-08 20:11:45.605013 INFO::Fitting model to feature number 1776, ASV_2474
#> 2023-05-08 20:11:45.608113 INFO::Fitting model to feature number 1777, ASV_1462
#> 2023-05-08 20:11:45.611276 INFO::Fitting model to feature number 1778, ASV_2615
#> 2023-05-08 20:11:45.614875 INFO::Fitting model to feature number 1779, ASV_581
#> 2023-05-08 20:11:45.618252 INFO::Fitting model to feature number 1780, ASV_1909
#> 2023-05-08 20:11:45.621425 INFO::Fitting model to feature number 1781, ASV_1267
#> 2023-05-08 20:11:45.624501 INFO::Fitting model to feature number 1782, ASV_669
#> 2023-05-08 20:11:45.627983 INFO::Fitting model to feature number 1783, ASV_1054
#> 2023-05-08 20:11:45.631488 INFO::Fitting model to feature number 1784, ASV_1882
#> 2023-05-08 20:11:45.634778 INFO::Fitting model to feature number 1785, ASV_1329
#> 2023-05-08 20:11:45.63815 INFO::Fitting model to feature number 1786, ASV_191
#> 2023-05-08 20:11:45.641423 INFO::Fitting model to feature number 1787, ASV_574
#> 2023-05-08 20:11:45.644977 INFO::Fitting model to feature number 1788, ASV_2319
#> 2023-05-08 20:11:45.648146 INFO::Fitting model to feature number 1789, ASV_2477
#> 2023-05-08 20:11:45.651205 INFO::Fitting model to feature number 1790, ASV_1969
#> 2023-05-08 20:11:45.654468 INFO::Fitting model to feature number 1791, ASV_2494
#> 2023-05-08 20:11:45.657782 INFO::Fitting model to feature number 1792, ASV_2578
#> 2023-05-08 20:11:45.661806 INFO::Fitting model to feature number 1793, ASV_272
#> 2023-05-08 20:11:45.665295 INFO::Fitting model to feature number 1794, ASV_1079
#> 2023-05-08 20:11:45.668427 INFO::Fitting model to feature number 1795, ASV_2041
#> 2023-05-08 20:11:45.671457 INFO::Fitting model to feature number 1796, ASV_1596
#> 2023-05-08 20:11:45.67492 INFO::Fitting model to feature number 1797, ASV_329
#> 2023-05-08 20:11:45.678122 INFO::Fitting model to feature number 1798, ASV_2103
#> 2023-05-08 20:11:45.681159 INFO::Fitting model to feature number 1799, ASV_2648
#> 2023-05-08 20:11:45.684156 INFO::Fitting model to feature number 1800, ASV_1336
#> 2023-05-08 20:11:45.687192 INFO::Fitting model to feature number 1801, ASV_2469
#> 2023-05-08 20:11:45.690411 INFO::Fitting model to feature number 1802, ASV_469
#> 2023-05-08 20:11:45.693818 INFO::Fitting model to feature number 1803, ASV_1447
#> 2023-05-08 20:11:45.696916 INFO::Fitting model to feature number 1804, ASV_168
#> 2023-05-08 20:11:45.699934 INFO::Fitting model to feature number 1805, ASV_658
#> 2023-05-08 20:11:45.702933 INFO::Fitting model to feature number 1806, ASV_1558
#> 2023-05-08 20:11:45.7065 INFO::Fitting model to feature number 1807, ASV_2115
#> 2023-05-08 20:11:45.709686 INFO::Fitting model to feature number 1808, ASV_995
#> 2023-05-08 20:11:45.712757 INFO::Fitting model to feature number 1809, ASV_541
#> 2023-05-08 20:11:45.715795 INFO::Fitting model to feature number 1810, ASV_348
#> 2023-05-08 20:11:45.718805 INFO::Fitting model to feature number 1811, ASV_475
#> 2023-05-08 20:11:45.722398 INFO::Fitting model to feature number 1812, ASV_1730
#> 2023-05-08 20:11:45.725784 INFO::Fitting model to feature number 1813, ASV_717
#> 2023-05-08 20:11:45.728959 INFO::Fitting model to feature number 1814, ASV_823
#> 2023-05-08 20:11:45.732291 INFO::Fitting model to feature number 1815, ASV_1845
#> 2023-05-08 20:11:45.735595 INFO::Fitting model to feature number 1816, ASV_1539
#> 2023-05-08 20:11:45.73903 INFO::Fitting model to feature number 1817, ASV_2099
#> 2023-05-08 20:11:45.743312 INFO::Fitting model to feature number 1818, ASV_302
#> 2023-05-08 20:11:45.746726 INFO::Fitting model to feature number 1819, ASV_1005
#> 2023-05-08 20:11:45.749744 INFO::Fitting model to feature number 1820, ASV_1634
#> 2023-05-08 20:11:45.753099 INFO::Fitting model to feature number 1821, ASV_1825
#> 2023-05-08 20:11:45.756342 INFO::Fitting model to feature number 1822, ASV_1992
#> 2023-05-08 20:11:45.759438 INFO::Fitting model to feature number 1823, ASV_984
#> 2023-05-08 20:11:45.76255 INFO::Fitting model to feature number 1824, ASV_1451
#> 2023-05-08 20:11:45.765945 INFO::Fitting model to feature number 1825, ASV_1533
#> 2023-05-08 20:11:45.769141 INFO::Fitting model to feature number 1826, ASV_2471
#> 2023-05-08 20:11:45.772252 INFO::Fitting model to feature number 1827, ASV_2659
#> 2023-05-08 20:11:45.77532 INFO::Fitting model to feature number 1828, ASV_162
#> 2023-05-08 20:11:45.778367 INFO::Fitting model to feature number 1829, ASV_1409
#> 2023-05-08 20:11:45.781694 INFO::Fitting model to feature number 1830, ASV_2594
#> 2023-05-08 20:11:45.784948 INFO::Fitting model to feature number 1831, ASV_927
#> 2023-05-08 20:11:45.787997 INFO::Fitting model to feature number 1832, ASV_2670
#> 2023-05-08 20:11:45.791173 INFO::Fitting model to feature number 1833, ASV_1638
#> 2023-05-08 20:11:45.794171 INFO::Fitting model to feature number 1834, ASV_1919
#> 2023-05-08 20:11:45.797281 INFO::Fitting model to feature number 1835, ASV_729
#> 2023-05-08 20:11:45.801214 INFO::Fitting model to feature number 1836, ASV_1374
#> 2023-05-08 20:11:45.805142 INFO::Fitting model to feature number 1837, ASV_546
#> 2023-05-08 20:11:45.808367 INFO::Fitting model to feature number 1838, ASV_1353
#> 2023-05-08 20:11:45.811473 INFO::Fitting model to feature number 1839, ASV_1568
#> 2023-05-08 20:11:45.81456 INFO::Fitting model to feature number 1840, ASV_2458
#> 2023-05-08 20:11:45.818024 INFO::Fitting model to feature number 1841, ASV_2626
#> 2023-05-08 20:11:45.82124 INFO::Fitting model to feature number 1842, ASV_2645
#> 2023-05-08 20:11:45.824444 INFO::Fitting model to feature number 1843, ASV_1787
#> 2023-05-08 20:11:45.827663 INFO::Fitting model to feature number 1844, ASV_2347
#> 2023-05-08 20:11:45.830823 INFO::Fitting model to feature number 1845, ASV_2283
#> 2023-05-08 20:11:45.834111 INFO::Fitting model to feature number 1846, ASV_2582
#> 2023-05-08 20:11:45.837407 INFO::Fitting model to feature number 1847, ASV_1485
#> 2023-05-08 20:11:45.840861 INFO::Fitting model to feature number 1848, ASV_2872
#> 2023-05-08 20:11:45.844159 INFO::Fitting model to feature number 1849, ASV_1917
#> 2023-05-08 20:11:45.847326 INFO::Fitting model to feature number 1850, ASV_2267
#> 2023-05-08 20:11:45.850756 INFO::Fitting model to feature number 1851, ASV_170
#> 2023-05-08 20:11:45.85409 INFO::Fitting model to feature number 1852, ASV_867
#> 2023-05-08 20:11:45.857253 INFO::Fitting model to feature number 1853, ASV_1978
#> 2023-05-08 20:11:45.860343 INFO::Fitting model to feature number 1854, ASV_2387
#> 2023-05-08 20:11:45.863642 INFO::Fitting model to feature number 1855, ASV_599
#> 2023-05-08 20:11:45.866796 INFO::Fitting model to feature number 1856, ASV_1613
#> 2023-05-08 20:11:45.869991 INFO::Fitting model to feature number 1857, ASV_816
#> 2023-05-08 20:11:45.873398 INFO::Fitting model to feature number 1858, ASV_609
#> 2023-05-08 20:11:45.876742 INFO::Fitting model to feature number 1859, ASV_719
#> 2023-05-08 20:11:45.880348 INFO::Fitting model to feature number 1860, ASV_2106
#> 2023-05-08 20:11:45.883641 INFO::Fitting model to feature number 1861, ASV_1037
#> 2023-05-08 20:11:45.886784 INFO::Fitting model to feature number 1862, ASV_914
#> 2023-05-08 20:11:45.890377 INFO::Fitting model to feature number 1863, ASV_2091
#> 2023-05-08 20:11:45.893539 INFO::Fitting model to feature number 1864, ASV_2262
#> 2023-05-08 20:11:45.89712 INFO::Fitting model to feature number 1865, ASV_646
#> 2023-05-08 20:11:45.900678 INFO::Fitting model to feature number 1866, ASV_667
#> 2023-05-08 20:11:45.90395 INFO::Fitting model to feature number 1867, ASV_2274
#> 2023-05-08 20:11:45.907308 INFO::Fitting model to feature number 1868, ASV_173
#> 2023-05-08 20:11:45.91059 INFO::Fitting model to feature number 1869, ASV_654
#> 2023-05-08 20:11:45.914026 INFO::Fitting model to feature number 1870, ASV_744
#> 2023-05-08 20:11:45.917123 INFO::Fitting model to feature number 1871, ASV_1145
#> 2023-05-08 20:11:45.920377 INFO::Fitting model to feature number 1872, ASV_2528
#> 2023-05-08 20:11:45.923836 INFO::Fitting model to feature number 1873, ASV_671
#> 2023-05-08 20:11:45.927098 INFO::Fitting model to feature number 1874, ASV_2726
#> 2023-05-08 20:11:45.930289 INFO::Fitting model to feature number 1875, ASV_1657
#> 2023-05-08 20:11:45.93337 INFO::Fitting model to feature number 1876, ASV_1802
#> 2023-05-08 20:11:45.936451 INFO::Fitting model to feature number 1877, ASV_2897
#> 2023-05-08 20:11:45.939435 INFO::Fitting model to feature number 1878, ASV_1801
#> 2023-05-08 20:11:45.943029 INFO::Fitting model to feature number 1879, ASV_2197
#> 2023-05-08 20:11:45.946433 INFO::Fitting model to feature number 1880, ASV_354
#> 2023-05-08 20:11:45.949618 INFO::Fitting model to feature number 1881, ASV_1250
#> 2023-05-08 20:11:45.952737 INFO::Fitting model to feature number 1882, ASV_1508
#> 2023-05-08 20:11:45.955815 INFO::Fitting model to feature number 1883, ASV_2289
#> 2023-05-08 20:11:45.959204 INFO::Fitting model to feature number 1884, ASV_829
#> 2023-05-08 20:11:45.962526 INFO::Fitting model to feature number 1885, ASV_1905
#> 2023-05-08 20:11:45.965653 INFO::Fitting model to feature number 1886, ASV_2188
#> 2023-05-08 20:11:45.968756 INFO::Fitting model to feature number 1887, ASV_2700
#> 2023-05-08 20:11:45.97183 INFO::Fitting model to feature number 1888, ASV_602
#> 2023-05-08 20:11:45.975061 INFO::Fitting model to feature number 1889, ASV_662
#> 2023-05-08 20:11:45.97817 INFO::Fitting model to feature number 1890, ASV_1985
#> 2023-05-08 20:11:45.981195 INFO::Fitting model to feature number 1891, ASV_2158
#> 2023-05-08 20:11:45.984189 INFO::Fitting model to feature number 1892, ASV_740
#> 2023-05-08 20:11:45.987148 INFO::Fitting model to feature number 1893, ASV_849
#> 2023-05-08 20:11:45.990306 INFO::Fitting model to feature number 1894, ASV_881
#> 2023-05-08 20:11:45.993478 INFO::Fitting model to feature number 1895, ASV_2178
#> 2023-05-08 20:11:45.99654 INFO::Fitting model to feature number 1896, ASV_2001
#> 2023-05-08 20:11:45.999567 INFO::Fitting model to feature number 1897, ASV_2705
#> 2023-05-08 20:11:46.002563 INFO::Fitting model to feature number 1898, ASV_830
#> 2023-05-08 20:11:46.005837 INFO::Fitting model to feature number 1899, ASV_1547
#> 2023-05-08 20:11:46.00908 INFO::Fitting model to feature number 1900, ASV_2840
#> 2023-05-08 20:11:46.012312 INFO::Fitting model to feature number 1901, ASV_2561
#> 2023-05-08 20:11:46.015466 INFO::Fitting model to feature number 1902, ASV_2184
#> 2023-05-08 20:11:46.01853 INFO::Fitting model to feature number 1903, ASV_1573
#> 2023-05-08 20:11:46.022381 INFO::Fitting model to feature number 1904, ASV_2083
#> 2023-05-08 20:11:46.025854 INFO::Fitting model to feature number 1905, ASV_2084
#> 2023-05-08 20:11:46.029032 INFO::Fitting model to feature number 1906, ASV_2683
#> 2023-05-08 20:11:46.032045 INFO::Fitting model to feature number 1907, ASV_1849
#> 2023-05-08 20:11:46.035252 INFO::Fitting model to feature number 1908, ASV_1892
#> 2023-05-08 20:11:46.038509 INFO::Fitting model to feature number 1909, ASV_2346
#> 2023-05-08 20:11:46.042008 INFO::Fitting model to feature number 1910, ASV_2901
#> 2023-05-08 20:11:46.045466 INFO::Fitting model to feature number 1911, ASV_2717
#> 2023-05-08 20:11:46.04884 INFO::Fitting model to feature number 1912, ASV_2277
#> 2023-05-08 20:11:46.052159 INFO::Fitting model to feature number 1913, ASV_2538
#> 2023-05-08 20:11:46.05558 INFO::Fitting model to feature number 1914, ASV_2552
#> 2023-05-08 20:11:46.058906 INFO::Fitting model to feature number 1915, ASV_2449
#> 2023-05-08 20:11:46.062052 INFO::Fitting model to feature number 1916, ASV_2880
#> 2023-05-08 20:11:46.06511 INFO::Fitting model to feature number 1917, ASV_194
#> 2023-05-08 20:11:46.068252 INFO::Fitting model to feature number 1918, ASV_395
#> 2023-05-08 20:11:46.071386 INFO::Fitting model to feature number 1919, ASV_205
#> 2023-05-08 20:11:46.074444 INFO::Fitting model to feature number 1920, ASV_313
#> 2023-05-08 20:11:46.077491 INFO::Fitting model to feature number 1921, ASV_686
#> 2023-05-08 20:11:46.08068 INFO::Fitting model to feature number 1922, ASV_309
#> 2023-05-08 20:11:46.08388 INFO::Fitting model to feature number 1923, ASV_341
#> 2023-05-08 20:11:46.087218 INFO::Fitting model to feature number 1924, ASV_514
#> 2023-05-08 20:11:46.09042 INFO::Fitting model to feature number 1925, ASV_824
#> 2023-05-08 20:11:46.093572 INFO::Fitting model to feature number 1926, ASV_1572
#> 2023-05-08 20:11:46.096779 INFO::Fitting model to feature number 1927, ASV_548
#> 2023-05-08 20:11:46.100184 INFO::Fitting model to feature number 1928, ASV_1127
#> 2023-05-08 20:11:46.103475 INFO::Fitting model to feature number 1929, ASV_1974
#> 2023-05-08 20:11:46.106825 INFO::Fitting model to feature number 1930, ASV_1615
#> 2023-05-08 20:11:46.110441 INFO::Fitting model to feature number 1931, ASV_723
#> 2023-05-08 20:11:46.11366 INFO::Fitting model to feature number 1932, ASV_2187
#> 2023-05-08 20:11:46.11739 INFO::Fitting model to feature number 1933, ASV_1183
#> 2023-05-08 20:11:46.120627 INFO::Fitting model to feature number 1934, ASV_519
#> 2023-05-08 20:11:46.124469 INFO::Fitting model to feature number 1935, ASV_2013
#> 2023-05-08 20:11:46.127594 INFO::Fitting model to feature number 1936, ASV_594
#> 2023-05-08 20:11:46.130962 INFO::Fitting model to feature number 1937, ASV_714
#> 2023-05-08 20:11:46.134251 INFO::Fitting model to feature number 1938, ASV_2434
#> 2023-05-08 20:11:46.137317 INFO::Fitting model to feature number 1939, ASV_617
#> 2023-05-08 20:11:46.140373 INFO::Fitting model to feature number 1940, ASV_1947
#> 2023-05-08 20:11:46.143649 INFO::Fitting model to feature number 1941, ASV_561
#> 2023-05-08 20:11:46.146674 INFO::Fitting model to feature number 1942, ASV_860
#> 2023-05-08 20:11:46.14983 INFO::Fitting model to feature number 1943, ASV_1035
#> 2023-05-08 20:11:46.152861 INFO::Fitting model to feature number 1944, ASV_2678
#> 2023-05-08 20:11:46.155893 INFO::Fitting model to feature number 1945, ASV_2781
#> 2023-05-08 20:11:46.15908 INFO::Fitting model to feature number 1946, ASV_2162
#> 2023-05-08 20:11:46.162135 INFO::Fitting model to feature number 1947, ASV_1198
#> 2023-05-08 20:11:46.165618 INFO::Fitting model to feature number 1948, ASV_2405
#> 2023-05-08 20:11:46.16878 INFO::Fitting model to feature number 1949, ASV_1367
#> 2023-05-08 20:11:46.171935 INFO::Fitting model to feature number 1950, ASV_1644
#> 2023-05-08 20:11:46.175583 INFO::Fitting model to feature number 1951, ASV_1994
#> 2023-05-08 20:11:46.179295 INFO::Fitting model to feature number 1952, ASV_2332
#> 2023-05-08 20:11:46.182727 INFO::Fitting model to feature number 1953, ASV_204
#> 2023-05-08 20:11:46.185849 INFO::Fitting model to feature number 1954, ASV_845
#> 2023-05-08 20:11:46.189082 INFO::Fitting model to feature number 1955, ASV_944
#> 2023-05-08 20:11:46.192223 INFO::Fitting model to feature number 1956, ASV_225
#> 2023-05-08 20:11:46.195549 INFO::Fitting model to feature number 1957, ASV_2619
#> 2023-05-08 20:11:46.198696 INFO::Fitting model to feature number 1958, ASV_711
#> 2023-05-08 20:11:46.201725 INFO::Fitting model to feature number 1959, ASV_2782
#> 2023-05-08 20:11:46.204728 INFO::Fitting model to feature number 1960, ASV_2894
#> 2023-05-08 20:11:46.207745 INFO::Fitting model to feature number 1961, ASV_249
#> 2023-05-08 20:11:46.211194 INFO::Fitting model to feature number 1962, ASV_1516
#> 2023-05-08 20:11:46.214477 INFO::Fitting model to feature number 1963, ASV_695
#> 2023-05-08 20:11:46.218288 INFO::Fitting model to feature number 1964, ASV_1461
#> 2023-05-08 20:11:46.221736 INFO::Fitting model to feature number 1965, ASV_2345
#> 2023-05-08 20:11:46.225181 INFO::Fitting model to feature number 1966, ASV_1316
#> 2023-05-08 20:11:46.2286 INFO::Fitting model to feature number 1967, ASV_1560
#> 2023-05-08 20:11:46.231819 INFO::Fitting model to feature number 1968, ASV_2450
#> 2023-05-08 20:11:46.235214 INFO::Fitting model to feature number 1969, ASV_230
#> 2023-05-08 20:11:46.238429 INFO::Fitting model to feature number 1970, ASV_587
#> 2023-05-08 20:11:46.241885 INFO::Fitting model to feature number 1971, ASV_1117
#> 2023-05-08 20:11:46.245107 INFO::Fitting model to feature number 1972, ASV_334
#> 2023-05-08 20:11:46.248276 INFO::Fitting model to feature number 1973, ASV_638
#> 2023-05-08 20:11:46.251516 INFO::Fitting model to feature number 1974, ASV_1570
#> 2023-05-08 20:11:46.255265 INFO::Fitting model to feature number 1975, ASV_431
#> 2023-05-08 20:11:46.258749 INFO::Fitting model to feature number 1976, ASV_1726
#> 2023-05-08 20:11:46.262113 INFO::Fitting model to feature number 1977, ASV_2774
#> 2023-05-08 20:11:46.265414 INFO::Fitting model to feature number 1978, ASV_2786
#> 2023-05-08 20:11:46.268566 INFO::Fitting model to feature number 1979, ASV_236
#> 2023-05-08 20:11:46.272738 INFO::Fitting model to feature number 1980, ASV_437
#> 2023-05-08 20:11:46.276202 INFO::Fitting model to feature number 1981, ASV_743
#> 2023-05-08 20:11:46.279456 INFO::Fitting model to feature number 1982, ASV_2127
#> 2023-05-08 20:11:46.282704 INFO::Fitting model to feature number 1983, ASV_1513
#> 2023-05-08 20:11:46.286165 INFO::Fitting model to feature number 1984, ASV_1144
#> 2023-05-08 20:11:46.28946 INFO::Fitting model to feature number 1985, ASV_679
#> 2023-05-08 20:11:46.2928 INFO::Fitting model to feature number 1986, ASV_1284
#> 2023-05-08 20:11:46.29602 INFO::Fitting model to feature number 1987, ASV_2616
#> 2023-05-08 20:11:46.299159 INFO::Fitting model to feature number 1988, ASV_1123
#> 2023-05-08 20:11:46.302712 INFO::Fitting model to feature number 1989, ASV_1260
#> 2023-05-08 20:11:46.306209 INFO::Fitting model to feature number 1990, ASV_608
#> 2023-05-08 20:11:46.309736 INFO::Fitting model to feature number 1991, ASV_1252
#> 2023-05-08 20:11:46.312968 INFO::Fitting model to feature number 1992, ASV_1499
#> 2023-05-08 20:11:46.316026 INFO::Fitting model to feature number 1993, ASV_1964
#> 2023-05-08 20:11:46.3195 INFO::Fitting model to feature number 1994, ASV_629
#> 2023-05-08 20:11:46.322762 INFO::Fitting model to feature number 1995, ASV_1540
#> 2023-05-08 20:11:46.325962 INFO::Fitting model to feature number 1996, ASV_786
#> 2023-05-08 20:11:46.329302 INFO::Fitting model to feature number 1997, ASV_1716
#> 2023-05-08 20:11:46.33272 INFO::Fitting model to feature number 1998, ASV_1174
#> 2023-05-08 20:11:46.336579 INFO::Fitting model to feature number 1999, ASV_769
#> 2023-05-08 20:11:46.339812 INFO::Fitting model to feature number 2000, ASV_1182
#> 2023-05-08 20:11:46.342936 INFO::Fitting model to feature number 2001, ASV_510
#> 2023-05-08 20:11:46.345981 INFO::Fitting model to feature number 2002, ASV_637
#> 2023-05-08 20:11:46.34903 INFO::Fitting model to feature number 2003, ASV_1023
#> 2023-05-08 20:11:46.352288 INFO::Fitting model to feature number 2004, ASV_1290
#> 2023-05-08 20:11:46.355709 INFO::Fitting model to feature number 2005, ASV_1430
#> 2023-05-08 20:11:46.358866 INFO::Fitting model to feature number 2006, ASV_1629
#> 2023-05-08 20:11:46.362064 INFO::Fitting model to feature number 2007, ASV_1776
#> 2023-05-08 20:11:46.365502 INFO::Fitting model to feature number 2008, ASV_2721
#> 2023-05-08 20:11:46.36877 INFO::Fitting model to feature number 2009, ASV_1609
#> 2023-05-08 20:11:46.372187 INFO::Fitting model to feature number 2010, ASV_2005
#> 2023-05-08 20:11:46.37552 INFO::Fitting model to feature number 2011, ASV_2022
#> 2023-05-08 20:11:46.378686 INFO::Fitting model to feature number 2012, ASV_2630
#> 2023-05-08 20:11:46.381814 INFO::Fitting model to feature number 2013, ASV_694
#> 2023-05-08 20:11:46.385236 INFO::Fitting model to feature number 2014, ASV_1318
#> 2023-05-08 20:11:46.388506 INFO::Fitting model to feature number 2015, ASV_2212
#> 2023-05-08 20:11:46.391742 INFO::Fitting model to feature number 2016, ASV_2372
#> 2023-05-08 20:11:46.394993 INFO::Fitting model to feature number 2017, ASV_2419
#> 2023-05-08 20:11:46.398212 INFO::Fitting model to feature number 2018, ASV_2559
#> 2023-05-08 20:11:46.401507 INFO::Fitting model to feature number 2019, ASV_898
#> 2023-05-08 20:11:46.405253 INFO::Fitting model to feature number 2020, ASV_2814
#> 2023-05-08 20:11:46.408765 INFO::Fitting model to feature number 2021, ASV_1855
#> 2023-05-08 20:11:46.412557 INFO::Fitting model to feature number 2022, ASV_2809
#> 2023-05-08 20:11:46.416094 INFO::Fitting model to feature number 2023, ASV_288
#> 2023-05-08 20:11:46.419428 INFO::Fitting model to feature number 2024, ASV_772
#> 2023-05-08 20:11:46.422672 INFO::Fitting model to feature number 2025, ASV_1668
#> 2023-05-08 20:11:46.426029 INFO::Fitting model to feature number 2026, ASV_2048
#> 2023-05-08 20:11:46.429437 INFO::Fitting model to feature number 2027, ASV_1929
#> 2023-05-08 20:11:46.43258 INFO::Fitting model to feature number 2028, ASV_404
#> 2023-05-08 20:11:46.4356 INFO::Fitting model to feature number 2029, ASV_1465
#> 2023-05-08 20:11:46.438602 INFO::Fitting model to feature number 2030, ASV_497
#> 2023-05-08 20:11:46.441777 INFO::Fitting model to feature number 2031, ASV_922
#> 2023-05-08 20:11:46.444995 INFO::Fitting model to feature number 2032, ASV_1410
#> 2023-05-08 20:11:46.448304 INFO::Fitting model to feature number 2033, ASV_992
#> 2023-05-08 20:11:46.451366 INFO::Fitting model to feature number 2034, ASV_1057
#> 2023-05-08 20:11:46.454704 INFO::Fitting model to feature number 2035, ASV_1666
#> 2023-05-08 20:11:46.458377 INFO::Fitting model to feature number 2036, ASV_1352
#> 2023-05-08 20:11:46.461634 INFO::Fitting model to feature number 2037, ASV_1468
#> 2023-05-08 20:11:46.464757 INFO::Fitting model to feature number 2038, ASV_870
#> 2023-05-08 20:11:46.467875 INFO::Fitting model to feature number 2039, ASV_1270
#> 2023-05-08 20:11:46.47096 INFO::Fitting model to feature number 2040, ASV_1076
#> 2023-05-08 20:11:46.474222 INFO::Fitting model to feature number 2041, ASV_1769
#> 2023-05-08 20:11:46.477401 INFO::Fitting model to feature number 2042, ASV_1879
#> 2023-05-08 20:11:46.480575 INFO::Fitting model to feature number 2043, ASV_536
#> 2023-05-08 20:11:46.483583 INFO::Fitting model to feature number 2044, ASV_2208
#> 2023-05-08 20:11:46.486592 INFO::Fitting model to feature number 2045, ASV_356
#> 2023-05-08 20:11:46.490014 INFO::Fitting model to feature number 2046, ASV_1691
#> 2023-05-08 20:11:46.494311 INFO::Fitting model to feature number 2047, ASV_673
#> 2023-05-08 20:11:46.497653 INFO::Fitting model to feature number 2048, ASV_1302
#> 2023-05-08 20:11:46.50087 INFO::Fitting model to feature number 2049, ASV_2156
#> 2023-05-08 20:11:46.503929 INFO::Fitting model to feature number 2050, ASV_2818
#> 2023-05-08 20:11:46.507361 INFO::Fitting model to feature number 2051, ASV_833
#> 2023-05-08 20:11:46.51101 INFO::Fitting model to feature number 2052, ASV_1379
#> 2023-05-08 20:11:46.514425 INFO::Fitting model to feature number 2053, ASV_1811
#> 2023-05-08 20:11:46.517821 INFO::Fitting model to feature number 2054, ASV_2784
#> 2023-05-08 20:11:46.52112 INFO::Fitting model to feature number 2055, ASV_2060
#> 2023-05-08 20:11:46.524818 INFO::Fitting model to feature number 2056, ASV_255
#> 2023-05-08 20:11:46.527965 INFO::Fitting model to feature number 2057, ASV_1130
#> 2023-05-08 20:11:46.531045 INFO::Fitting model to feature number 2058, ASV_2425
#> 2023-05-08 20:11:46.534543 INFO::Fitting model to feature number 2059, ASV_2842
#> 2023-05-08 20:11:46.537879 INFO::Fitting model to feature number 2060, ASV_1502
#> 2023-05-08 20:11:46.541243 INFO::Fitting model to feature number 2061, ASV_1833
#> 2023-05-08 20:11:46.544321 INFO::Fitting model to feature number 2062, ASV_1732
#> 2023-05-08 20:11:46.547363 INFO::Fitting model to feature number 2063, ASV_2617
#> 2023-05-08 20:11:46.55064 INFO::Fitting model to feature number 2064, ASV_900
#> 2023-05-08 20:11:46.555103 INFO::Fitting model to feature number 2065, ASV_2681
#> 2023-05-08 20:11:46.558843 INFO::Fitting model to feature number 2066, ASV_2804
#> 2023-05-08 20:11:46.562291 INFO::Fitting model to feature number 2067, ASV_1519
#> 2023-05-08 20:11:46.565585 INFO::Fitting model to feature number 2068, ASV_1790
#> 2023-05-08 20:11:46.569021 INFO::Fitting model to feature number 2069, ASV_2765
#> 2023-05-08 20:11:46.572186 INFO::Fitting model to feature number 2070, ASV_1885
#> 2023-05-08 20:11:46.575286 INFO::Fitting model to feature number 2071, ASV_2793
#> 2023-05-08 20:11:46.578365 INFO::Fitting model to feature number 2072, ASV_2792
#> 2023-05-08 20:11:46.58148 INFO::Fitting model to feature number 2073, ASV_271
#> 2023-05-08 20:11:46.585294 INFO::Fitting model to feature number 2074, ASV_1836
#> 2023-05-08 20:11:46.58919 INFO::Fitting model to feature number 2075, ASV_2276
#> 2023-05-08 20:11:46.592698 INFO::Fitting model to feature number 2076, ASV_2393
#> 2023-05-08 20:11:46.595976 INFO::Fitting model to feature number 2077, ASV_1358
#> 2023-05-08 20:11:46.599487 INFO::Fitting model to feature number 2078, ASV_1883
#> 2023-05-08 20:11:46.602809 INFO::Fitting model to feature number 2079, ASV_1918
#> 2023-05-08 20:11:46.605951 INFO::Fitting model to feature number 2080, ASV_1697
#> 2023-05-08 20:11:46.609172 INFO::Fitting model to feature number 2081, ASV_1758
#> 2023-05-08 20:11:46.61259 INFO::Fitting model to feature number 2082, ASV_648
#> 2023-05-08 20:11:46.615723 INFO::Fitting model to feature number 2083, ASV_1256
#> 2023-05-08 20:11:46.61879 INFO::Fitting model to feature number 2084, ASV_1261
#> 2023-05-08 20:11:46.621908 INFO::Fitting model to feature number 2085, ASV_2310
#> 2023-05-08 20:11:46.625602 INFO::Fitting model to feature number 2086, ASV_2612
#> 2023-05-08 20:11:46.629536 INFO::Fitting model to feature number 2087, ASV_556
#> 2023-05-08 20:11:46.632988 INFO::Fitting model to feature number 2088, ASV_1294
#> 2023-05-08 20:11:46.636317 INFO::Fitting model to feature number 2089, ASV_2581
#> 2023-05-08 20:11:46.639699 INFO::Fitting model to feature number 2090, ASV_1443
#> 2023-05-08 20:11:46.643318 INFO::Fitting model to feature number 2091, ASV_972
#> 2023-05-08 20:11:46.646489 INFO::Fitting model to feature number 2092, ASV_1498
#> 2023-05-08 20:11:46.64953 INFO::Fitting model to feature number 2093, ASV_2460
#> 2023-05-08 20:11:46.652601 INFO::Fitting model to feature number 2094, ASV_1874
#> 2023-05-08 20:11:46.655677 INFO::Fitting model to feature number 2095, ASV_2123
#> 2023-05-08 20:11:46.659253 INFO::Fitting model to feature number 2096, ASV_563
#> 2023-05-08 20:11:46.663045 INFO::Fitting model to feature number 2097, ASV_2047
#> 2023-05-08 20:11:46.666167 INFO::Fitting model to feature number 2098, ASV_1238
#> 2023-05-08 20:11:46.669168 INFO::Fitting model to feature number 2099, ASV_1340
#> 2023-05-08 20:11:46.672182 INFO::Fitting model to feature number 2100, ASV_2344
#> 2023-05-08 20:11:46.675467 INFO::Fitting model to feature number 2101, ASV_838
#> 2023-05-08 20:11:46.678592 INFO::Fitting model to feature number 2102, ASV_910
#> 2023-05-08 20:11:46.681797 INFO::Fitting model to feature number 2103, ASV_1956
#> 2023-05-08 20:11:46.685191 INFO::Fitting model to feature number 2104, ASV_857
#> 2023-05-08 20:11:46.688341 INFO::Fitting model to feature number 2105, ASV_1922
#> 2023-05-08 20:11:46.69185 INFO::Fitting model to feature number 2106, ASV_2790
#> 2023-05-08 20:11:46.695072 INFO::Fitting model to feature number 2107, ASV_1887
#> 2023-05-08 20:11:46.698182 INFO::Fitting model to feature number 2108, ASV_2658
#> 2023-05-08 20:11:46.701237 INFO::Fitting model to feature number 2109, ASV_2646
#> 2023-05-08 20:11:46.704298 INFO::Fitting model to feature number 2110, ASV_1423
#> 2023-05-08 20:11:46.707788 INFO::Fitting model to feature number 2111, ASV_564
#> 2023-05-08 20:11:46.711237 INFO::Fitting model to feature number 2112, ASV_1306
#> 2023-05-08 20:11:46.714494 INFO::Fitting model to feature number 2113, ASV_1827
#> 2023-05-08 20:11:46.717582 INFO::Fitting model to feature number 2114, ASV_2886
#> 2023-05-08 20:11:46.720563 INFO::Fitting model to feature number 2115, ASV_297
#> 2023-05-08 20:11:46.724248 INFO::Fitting model to feature number 2116, ASV_1398
#> 2023-05-08 20:11:46.728412 INFO::Fitting model to feature number 2117, ASV_2413
#> 2023-05-08 20:11:46.732076 INFO::Fitting model to feature number 2118, ASV_1669
#> 2023-05-08 20:11:46.735321 INFO::Fitting model to feature number 2119, ASV_525
#> 2023-05-08 20:11:46.738509 INFO::Fitting model to feature number 2120, ASV_1491
#> 2023-05-08 20:11:46.74372 INFO::Fitting model to feature number 2121, ASV_1179
#> 2023-05-08 20:11:46.746912 INFO::Fitting model to feature number 2122, ASV_1550
#> 2023-05-08 20:11:46.750053 INFO::Fitting model to feature number 2123, ASV_1930
#> 2023-05-08 20:11:46.753136 INFO::Fitting model to feature number 2124, ASV_2367
#> 2023-05-08 20:11:46.756653 INFO::Fitting model to feature number 2125, ASV_1856
#> 2023-05-08 20:11:46.759779 INFO::Fitting model to feature number 2126, ASV_2213
#> 2023-05-08 20:11:46.7628 INFO::Fitting model to feature number 2127, ASV_323
#> 2023-05-08 20:11:46.765745 INFO::Fitting model to feature number 2128, ASV_2783
#> 2023-05-08 20:11:46.768704 INFO::Fitting model to feature number 2129, ASV_1515
#> 2023-05-08 20:11:46.772083 INFO::Fitting model to feature number 2130, ASV_1779
#> 2023-05-08 20:11:46.775656 INFO::Fitting model to feature number 2131, ASV_1626
#> 2023-05-08 20:11:46.779435 INFO::Fitting model to feature number 2132, ASV_2531
#> 2023-05-08 20:11:46.783541 INFO::Fitting model to feature number 2133, ASV_2780
#> 2023-05-08 20:11:46.787005 INFO::Fitting model to feature number 2134, ASV_1564
#> 2023-05-08 20:11:46.790557 INFO::Fitting model to feature number 2135, ASV_2715
#> 2023-05-08 20:11:46.793862 INFO::Fitting model to feature number 2136, ASV_2132
#> 2023-05-08 20:11:46.797022 INFO::Fitting model to feature number 2137, ASV_2637
#> 2023-05-08 20:11:46.800298 INFO::Fitting model to feature number 2138, ASV_520
#> 2023-05-08 20:11:46.803685 INFO::Fitting model to feature number 2139, ASV_1078
#> 2023-05-08 20:11:46.806898 INFO::Fitting model to feature number 2140, ASV_1187
#> 2023-05-08 20:11:46.809969 INFO::Fitting model to feature number 2141, ASV_2687
#> 2023-05-08 20:11:46.813046 INFO::Fitting model to feature number 2142, ASV_1171
#> 2023-05-08 20:11:46.816069 INFO::Fitting model to feature number 2143, ASV_1269
#> 2023-05-08 20:11:46.819112 INFO::Fitting model to feature number 2144, ASV_1712
#> 2023-05-08 20:11:46.822734 INFO::Fitting model to feature number 2145, ASV_1760
#> 2023-05-08 20:11:46.82617 INFO::Fitting model to feature number 2146, ASV_2759
#> 2023-05-08 20:11:46.829244 INFO::Fitting model to feature number 2147, ASV_2380
#> 2023-05-08 20:11:46.832496 INFO::Fitting model to feature number 2148, ASV_1642
#> 2023-05-08 20:11:46.836063 INFO::Fitting model to feature number 2149, ASV_2011
#> 2023-05-08 20:11:46.839412 INFO::Fitting model to feature number 2150, ASV_1429
#> 2023-05-08 20:11:46.842619 INFO::Fitting model to feature number 2151, ASV_2230
#> 2023-05-08 20:11:46.845899 INFO::Fitting model to feature number 2152, ASV_1458
#> 2023-05-08 20:11:46.850216 INFO::Fitting model to feature number 2153, ASV_1511
#> 2023-05-08 20:11:46.853625 INFO::Fitting model to feature number 2154, ASV_1865
#> 2023-05-08 20:11:46.857032 INFO::Fitting model to feature number 2155, ASV_2080
#> 2023-05-08 20:11:46.860244 INFO::Fitting model to feature number 2156, ASV_2662
#> 2023-05-08 20:11:46.863304 INFO::Fitting model to feature number 2157, ASV_281
#> 2023-05-08 20:11:46.866726 INFO::Fitting model to feature number 2158, ASV_1175
#> 2023-05-08 20:11:46.870313 INFO::Fitting model to feature number 2159, ASV_2343
#> 2023-05-08 20:11:46.873699 INFO::Fitting model to feature number 2160, ASV_2422
#> 2023-05-08 20:11:46.877057 INFO::Fitting model to feature number 2161, ASV_2720
#> 2023-05-08 20:11:46.880232 INFO::Fitting model to feature number 2162, ASV_2788
#> 2023-05-08 20:11:46.88391 INFO::Fitting model to feature number 2163, ASV_818
#> 2023-05-08 20:11:46.887237 INFO::Fitting model to feature number 2164, ASV_1814
#> 2023-05-08 20:11:46.890488 INFO::Fitting model to feature number 2165, ASV_1221
#> 2023-05-08 20:11:46.893862 INFO::Fitting model to feature number 2166, ASV_2825
#> 2023-05-08 20:11:46.897381 INFO::Fitting model to feature number 2167, ASV_1203
#> 2023-05-08 20:11:46.901109 INFO::Fitting model to feature number 2168, ASV_1958
#> 2023-05-08 20:11:46.904537 INFO::Fitting model to feature number 2169, ASV_2416
#> 2023-05-08 20:11:46.907853 INFO::Fitting model to feature number 2170, ASV_2799
#> 2023-05-08 20:11:46.911029 INFO::Fitting model to feature number 2171, ASV_2189
#> 2023-05-08 20:11:46.914416 INFO::Fitting model to feature number 2172, ASV_2495
#> 2023-05-08 20:11:46.91798 INFO::Fitting model to feature number 2173, ASV_299
#> 2023-05-08 20:11:46.92132 INFO::Fitting model to feature number 2174, ASV_1219
#> 2023-05-08 20:11:46.924564 INFO::Fitting model to feature number 2175, ASV_1255
#> 2023-05-08 20:11:46.927746 INFO::Fitting model to feature number 2176, ASV_1835
#> 2023-05-08 20:11:46.931263 INFO::Fitting model to feature number 2177, ASV_1636
#> 2023-05-08 20:11:46.93463 INFO::Fitting model to feature number 2178, ASV_1170
#> 2023-05-08 20:11:46.937919 INFO::Fitting model to feature number 2179, ASV_2392
#> 2023-05-08 20:11:46.941117 INFO::Fitting model to feature number 2180, ASV_2756
#> 2023-05-08 20:11:46.944321 INFO::Fitting model to feature number 2181, ASV_1714
#> 2023-05-08 20:11:46.947705 INFO::Fitting model to feature number 2182, ASV_1988
#> 2023-05-08 20:11:46.950953 INFO::Fitting model to feature number 2183, ASV_300
#> 2023-05-08 20:11:46.954051 INFO::Fitting model to feature number 2184, ASV_2209
#> 2023-05-08 20:11:46.95707 INFO::Fitting model to feature number 2185, ASV_2322
#> 2023-05-08 20:11:46.960399 INFO::Fitting model to feature number 2186, ASV_924
#> 2023-05-08 20:11:46.963804 INFO::Fitting model to feature number 2187, ASV_1422
#> 2023-05-08 20:11:46.967286 INFO::Fitting model to feature number 2188, ASV_974
#> 2023-05-08 20:11:46.970482 INFO::Fitting model to feature number 2189, ASV_1536
#> 2023-05-08 20:11:46.973816 INFO::Fitting model to feature number 2190, ASV_2251
#> 2023-05-08 20:11:46.977078 INFO::Fitting model to feature number 2191, ASV_1653
#> 2023-05-08 20:11:46.980501 INFO::Fitting model to feature number 2192, ASV_1693
#> 2023-05-08 20:11:46.98372 INFO::Fitting model to feature number 2193, ASV_2850
#> 2023-05-08 20:11:46.986826 INFO::Fitting model to feature number 2194, ASV_355
#> 2023-05-08 20:11:46.98994 INFO::Fitting model to feature number 2195, ASV_1075
#> 2023-05-08 20:11:46.993103 INFO::Fitting model to feature number 2196, ASV_2867
#> 2023-05-08 20:11:46.996399 INFO::Fitting model to feature number 2197, ASV_2650
#> 2023-05-08 20:11:46.999776 INFO::Fitting model to feature number 2198, ASV_472
#> 2023-05-08 20:11:47.003025 INFO::Fitting model to feature number 2199, ASV_1325
#> 2023-05-08 20:11:47.006091 INFO::Fitting model to feature number 2200, ASV_2119
#> 2023-05-08 20:11:47.009768 INFO::Fitting model to feature number 2201, ASV_2337
#> 2023-05-08 20:11:47.013283 INFO::Fitting model to feature number 2202, ASV_718
#> 2023-05-08 20:11:47.016537 INFO::Fitting model to feature number 2203, ASV_1342
#> 2023-05-08 20:11:47.019736 INFO::Fitting model to feature number 2204, ASV_2321
#> 2023-05-08 20:11:47.023421 INFO::Fitting model to feature number 2205, ASV_1134
#> 2023-05-08 20:11:47.027024 INFO::Fitting model to feature number 2206, ASV_2247
#> 2023-05-08 20:11:47.030242 INFO::Fitting model to feature number 2207, ASV_2522
#> 2023-05-08 20:11:47.03333 INFO::Fitting model to feature number 2208, ASV_2644
#> 2023-05-08 20:11:47.036355 INFO::Fitting model to feature number 2209, ASV_543
#> 2023-05-08 20:11:47.039513 INFO::Fitting model to feature number 2210, ASV_2052
#> 2023-05-08 20:11:47.042809 INFO::Fitting model to feature number 2211, ASV_795
#> 2023-05-08 20:11:47.045947 INFO::Fitting model to feature number 2212, ASV_2445
#> 2023-05-08 20:11:47.048975 INFO::Fitting model to feature number 2213, ASV_1197
#> 2023-05-08 20:11:47.051983 INFO::Fitting model to feature number 2214, ASV_1330
#> 2023-05-08 20:11:47.055104 INFO::Fitting model to feature number 2215, ASV_2497
#> 2023-05-08 20:11:47.058248 INFO::Fitting model to feature number 2216, ASV_2540
#> 2023-05-08 20:11:47.061305 INFO::Fitting model to feature number 2217, ASV_1173
#> 2023-05-08 20:11:47.064681 INFO::Fitting model to feature number 2218, ASV_1663
#> 2023-05-08 20:11:47.068045 INFO::Fitting model to feature number 2219, ASV_1420
#> 2023-05-08 20:11:47.071768 INFO::Fitting model to feature number 2220, ASV_2070
#> 2023-05-08 20:11:47.076702 INFO::Fitting model to feature number 2221, ASV_2623
#> 2023-05-08 20:11:47.079851 INFO::Fitting model to feature number 2222, ASV_1452
#> 2023-05-08 20:11:47.082953 INFO::Fitting model to feature number 2223, ASV_2771
#> 2023-05-08 20:11:47.086355 INFO::Fitting model to feature number 2224, ASV_685
#> 2023-05-08 20:11:47.089608 INFO::Fitting model to feature number 2225, ASV_2190
#> 2023-05-08 20:11:47.092772 INFO::Fitting model to feature number 2226, ASV_2217
#> 2023-05-08 20:11:47.095847 INFO::Fitting model to feature number 2227, ASV_2125
#> 2023-05-08 20:11:47.098857 INFO::Fitting model to feature number 2228, ASV_2150
#> 2023-05-08 20:11:47.102352 INFO::Fitting model to feature number 2229, ASV_1042
#> 2023-05-08 20:11:47.105689 INFO::Fitting model to feature number 2230, ASV_1848
#> 2023-05-08 20:11:47.108908 INFO::Fitting model to feature number 2231, ASV_1926
#> 2023-05-08 20:11:47.112076 INFO::Fitting model to feature number 2232, ASV_612
#> 2023-05-08 20:11:47.11542 INFO::Fitting model to feature number 2233, ASV_1328
#> 2023-05-08 20:11:47.118758 INFO::Fitting model to feature number 2234, ASV_2218
#> 2023-05-08 20:11:47.122034 INFO::Fitting model to feature number 2235, ASV_1136
#> 2023-05-08 20:11:47.125157 INFO::Fitting model to feature number 2236, ASV_2480
#> 2023-05-08 20:11:47.128231 INFO::Fitting model to feature number 2237, ASV_868
#> 2023-05-08 20:11:47.131327 INFO::Fitting model to feature number 2238, ASV_674
#> 2023-05-08 20:11:47.134535 INFO::Fitting model to feature number 2239, ASV_2357
#> 2023-05-08 20:11:47.137972 INFO::Fitting model to feature number 2240, ASV_1126
#> 2023-05-08 20:11:47.14103 INFO::Fitting model to feature number 2241, ASV_1266
#> 2023-05-08 20:11:47.144032 INFO::Fitting model to feature number 2242, ASV_1932
#> 2023-05-08 20:11:47.148472 INFO::Fitting model to feature number 2243, ASV_1376
#> 2023-05-08 20:11:47.152748 INFO::Fitting model to feature number 2244, ASV_2877
#> 2023-05-08 20:11:47.158102 INFO::Fitting model to feature number 2245, ASV_505
#> 2023-05-08 20:11:47.161487 INFO::Fitting model to feature number 2246, ASV_2293
#> 2023-05-08 20:11:47.165025 INFO::Fitting model to feature number 2247, ASV_1153
#> 2023-05-08 20:11:47.168421 INFO::Fitting model to feature number 2248, ASV_959
#> 2023-05-08 20:11:47.171754 INFO::Fitting model to feature number 2249, ASV_1789
#> 2023-05-08 20:11:47.175073 INFO::Fitting model to feature number 2250, ASV_701
#> 2023-05-08 20:11:47.178566 INFO::Fitting model to feature number 2251, ASV_2913
#> 2023-05-08 20:11:47.182003 INFO::Fitting model to feature number 2252, ASV_2778
#> 2023-05-08 20:11:47.18535 INFO::Fitting model to feature number 2253, ASV_2193
#> 2023-05-08 20:11:47.188576 INFO::Fitting model to feature number 2254, ASV_2589
#> 2023-05-08 20:11:47.191792 INFO::Fitting model to feature number 2255, ASV_305
#> 2023-05-08 20:11:47.194864 INFO::Fitting model to feature number 2256, ASV_1215
#> 2023-05-08 20:11:47.198009 INFO::Fitting model to feature number 2257, ASV_448
#> 2023-05-08 20:11:47.20116 INFO::Fitting model to feature number 2258, ASV_515
#> 2023-05-08 20:11:47.204289 INFO::Fitting model to feature number 2259, ASV_1088
#> 2023-05-08 20:11:47.207519 INFO::Fitting model to feature number 2260, ASV_2373
#> 2023-05-08 20:11:47.211068 INFO::Fitting model to feature number 2261, ASV_2058
#> 2023-05-08 20:11:47.214619 INFO::Fitting model to feature number 2262, ASV_691
#> 2023-05-08 20:11:47.218047 INFO::Fitting model to feature number 2263, ASV_1873
#> 2023-05-08 20:11:47.221342 INFO::Fitting model to feature number 2264, ASV_507
#> 2023-05-08 20:11:47.224686 INFO::Fitting model to feature number 2265, ASV_1416
#> 2023-05-08 20:11:47.228064 INFO::Fitting model to feature number 2266, ASV_1566
#> 2023-05-08 20:11:47.231569 INFO::Fitting model to feature number 2267, ASV_2634
#> 2023-05-08 20:11:47.234802 INFO::Fitting model to feature number 2268, ASV_2061
#> 2023-05-08 20:11:47.237981 INFO::Fitting model to feature number 2269, ASV_311
#> 2023-05-08 20:11:47.241222 INFO::Fitting model to feature number 2270, ASV_2192
#> 2023-05-08 20:11:47.244487 INFO::Fitting model to feature number 2271, ASV_2777
#> 2023-05-08 20:11:47.247598 INFO::Fitting model to feature number 2272, ASV_1640
#> 2023-05-08 20:11:47.250626 INFO::Fitting model to feature number 2273, ASV_2590
#> 2023-05-08 20:11:47.253629 INFO::Fitting model to feature number 2274, ASV_2068
#> 2023-05-08 20:11:47.256823 INFO::Fitting model to feature number 2275, ASV_2088
#> 2023-05-08 20:11:47.259932 INFO::Fitting model to feature number 2276, ASV_2165
#> 2023-05-08 20:11:47.263034 INFO::Fitting model to feature number 2277, ASV_2910
#> 2023-05-08 20:11:47.266083 INFO::Fitting model to feature number 2278, ASV_318
#> 2023-05-08 20:11:47.269087 INFO::Fitting model to feature number 2279, ASV_1031
#> 2023-05-08 20:11:47.27211 INFO::Fitting model to feature number 2280, ASV_1164
#> 2023-05-08 20:11:47.275091 INFO::Fitting model to feature number 2281, ASV_1257
#> 2023-05-08 20:11:47.278191 INFO::Fitting model to feature number 2282, ASV_1366
#> 2023-05-08 20:11:47.281279 INFO::Fitting model to feature number 2283, ASV_725
#> 2023-05-08 20:11:47.284282 INFO::Fitting model to feature number 2284, ASV_813
#> 2023-05-08 20:11:47.287419 INFO::Fitting model to feature number 2285, ASV_567
#> 2023-05-08 20:11:47.290368 INFO::Fitting model to feature number 2286, ASV_319
#> 2023-05-08 20:11:47.293745 INFO::Fitting model to feature number 2287, ASV_557
#> 2023-05-08 20:11:47.296878 INFO::Fitting model to feature number 2288, ASV_1507
#> 2023-05-08 20:11:47.299901 INFO::Fitting model to feature number 2289, ASV_1748
#> 2023-05-08 20:11:47.303001 INFO::Fitting model to feature number 2290, ASV_1383
#> 2023-05-08 20:11:47.306153 INFO::Fitting model to feature number 2291, ASV_2869
#> 2023-05-08 20:11:47.310011 INFO::Fitting model to feature number 2292, ASV_351
#> 2023-05-08 20:11:47.313406 INFO::Fitting model to feature number 2293, ASV_425
#> 2023-05-08 20:11:47.316625 INFO::Fitting model to feature number 2294, ASV_1449
#> 2023-05-08 20:11:47.319875 INFO::Fitting model to feature number 2295, ASV_2596
#> 2023-05-08 20:11:47.323054 INFO::Fitting model to feature number 2296, ASV_1058
#> 2023-05-08 20:11:47.326682 INFO::Fitting model to feature number 2297, ASV_2094
#> 2023-05-08 20:11:47.329899 INFO::Fitting model to feature number 2298, ASV_1725
#> 2023-05-08 20:11:47.33299 INFO::Fitting model to feature number 2299, ASV_2338
#> 2023-05-08 20:11:47.336015 INFO::Fitting model to feature number 2300, ASV_1415
#> 2023-05-08 20:11:47.339271 INFO::Fitting model to feature number 2301, ASV_2620
#> 2023-05-08 20:11:47.342582 INFO::Fitting model to feature number 2302, ASV_555
#> 2023-05-08 20:11:47.34584 INFO::Fitting model to feature number 2303, ASV_1051
#> 2023-05-08 20:11:47.348962 INFO::Fitting model to feature number 2304, ASV_1501
#> 2023-05-08 20:11:47.352142 INFO::Fitting model to feature number 2305, ASV_2476
#> 2023-05-08 20:11:47.35547 INFO::Fitting model to feature number 2306, ASV_2775
#> 2023-05-08 20:11:47.358765 INFO::Fitting model to feature number 2307, ASV_703
#> 2023-05-08 20:11:47.362118 INFO::Fitting model to feature number 2308, ASV_2730
#> 2023-05-08 20:11:47.365528 INFO::Fitting model to feature number 2309, ASV_871
#> 2023-05-08 20:11:47.36897 INFO::Fitting model to feature number 2310, ASV_2820
#> 2023-05-08 20:11:47.373696 INFO::Fitting model to feature number 2311, ASV_1165
#> 2023-05-08 20:11:47.37721 INFO::Fitting model to feature number 2312, ASV_1662
#> 2023-05-08 20:11:47.381228 INFO::Fitting model to feature number 2313, ASV_2204
#> 2023-05-08 20:11:47.384314 INFO::Fitting model to feature number 2314, ASV_1253
#> 2023-05-08 20:11:47.387646 INFO::Fitting model to feature number 2315, ASV_2066
#> 2023-05-08 20:11:47.391062 INFO::Fitting model to feature number 2316, ASV_1863
#> 2023-05-08 20:11:47.394239 INFO::Fitting model to feature number 2317, ASV_2513
#> 2023-05-08 20:11:47.397422 INFO::Fitting model to feature number 2318, ASV_2746
#> 2023-05-08 20:11:47.400735 INFO::Fitting model to feature number 2319, ASV_2459
#> 2023-05-08 20:11:47.404492 INFO::Fitting model to feature number 2320, ASV_1916
#> 2023-05-08 20:11:47.407893 INFO::Fitting model to feature number 2321, ASV_2541
#> 2023-05-08 20:11:47.411153 INFO::Fitting model to feature number 2322, ASV_415
#> 2023-05-08 20:11:47.41438 INFO::Fitting model to feature number 2323, ASV_1896
#> 2023-05-08 20:11:47.41772 INFO::Fitting model to feature number 2324, ASV_2725
#> 2023-05-08 20:11:47.420907 INFO::Fitting model to feature number 2325, ASV_1877
#> 2023-05-08 20:11:47.424225 INFO::Fitting model to feature number 2326, ASV_451
#> 2023-05-08 20:11:47.427431 INFO::Fitting model to feature number 2327, ASV_1991
#> 2023-05-08 20:11:47.4307 INFO::Fitting model to feature number 2328, ASV_459
#> 2023-05-08 20:11:47.434001 INFO::Fitting model to feature number 2329, ASV_1990
#> 2023-05-08 20:11:47.437157 INFO::Fitting model to feature number 2330, ASV_2302
#> 2023-05-08 20:11:47.440242 INFO::Fitting model to feature number 2331, ASV_2881
#> 2023-05-08 20:11:47.457497 INFO::Fitting model to feature number 2332, ASV_1043
#> 2023-05-08 20:11:47.461814 INFO::Fitting model to feature number 2333, ASV_1090
#> 2023-05-08 20:11:47.466183 INFO::Fitting model to feature number 2334, ASV_1773
#> 2023-05-08 20:11:47.47021 INFO::Fitting model to feature number 2335, ASV_2113
#> 2023-05-08 20:11:47.473971 INFO::Fitting model to feature number 2336, ASV_2795
#> 2023-05-08 20:11:47.47757 INFO::Fitting model to feature number 2337, ASV_1943
#> 2023-05-08 20:11:47.480894 INFO::Fitting model to feature number 2338, ASV_2509
#> 2023-05-08 20:11:47.48428 INFO::Fitting model to feature number 2339, ASV_1652
#> 2023-05-08 20:11:47.487818 INFO::Fitting model to feature number 2340, ASV_2633
#> 2023-05-08 20:11:47.491866 INFO::Fitting model to feature number 2341, ASV_2794
#> 2023-05-08 20:11:47.495279 INFO::Fitting model to feature number 2342, ASV_2829
#> 2023-05-08 20:11:47.498782 INFO::Fitting model to feature number 2343, ASV_2688
#> 2023-05-08 20:11:47.502203 INFO::Fitting model to feature number 2344, ASV_2176
#> 2023-05-08 20:11:47.505496 INFO::Fitting model to feature number 2345, ASV_1107
#> 2023-05-08 20:11:47.508823 INFO::Fitting model to feature number 2346, ASV_1545
#> 2023-05-08 20:11:47.512289 INFO::Fitting model to feature number 2347, ASV_1141
#> 2023-05-08 20:11:47.515833 INFO::Fitting model to feature number 2348, ASV_1621
#> 2023-05-08 20:11:47.519302 INFO::Fitting model to feature number 2349, ASV_1437
#> 2023-05-08 20:11:47.52274 INFO::Fitting model to feature number 2350, ASV_1242
#> 2023-05-08 20:11:47.526269 INFO::Fitting model to feature number 2351, ASV_470
#> 2023-05-08 20:11:47.529756 INFO::Fitting model to feature number 2352, ASV_1371
#> 2023-05-08 20:11:47.533095 INFO::Fitting model to feature number 2353, ASV_891
#> 2023-05-08 20:11:47.536988 INFO::Fitting model to feature number 2354, ASV_1528
#> 2023-05-08 20:11:47.540711 INFO::Fitting model to feature number 2355, ASV_1161
#> 2023-05-08 20:11:47.544466 INFO::Fitting model to feature number 2356, ASV_1670
#> 2023-05-08 20:11:47.547884 INFO::Fitting model to feature number 2357, ASV_468
#> 2023-05-08 20:11:47.551126 INFO::Fitting model to feature number 2358, ASV_1581
#> 2023-05-08 20:11:47.554292 INFO::Fitting model to feature number 2359, ASV_566
#> 2023-05-08 20:11:47.557664 INFO::Fitting model to feature number 2360, ASV_2028
#> 2023-05-08 20:11:47.561274 INFO::Fitting model to feature number 2361, ASV_2297
#> 2023-05-08 20:11:47.564679 INFO::Fitting model to feature number 2362, ASV_1348
#> 2023-05-08 20:11:47.568005 INFO::Fitting model to feature number 2363, ASV_1577
#> 2023-05-08 20:11:47.571093 INFO::Fitting model to feature number 2364, ASV_2220
#> 2023-05-08 20:11:47.57427 INFO::Fitting model to feature number 2365, ASV_471
#> 2023-05-08 20:11:47.577715 INFO::Fitting model to feature number 2366, ASV_677
#> 2023-05-08 20:11:47.580951 INFO::Fitting model to feature number 2367, ASV_2268
#> 2023-05-08 20:11:47.584227 INFO::Fitting model to feature number 2368, ASV_504
#> 2023-05-08 20:11:47.587325 INFO::Fitting model to feature number 2369, ASV_1229
#> 2023-05-08 20:11:47.592592 INFO::Fitting model to feature number 2370, ASV_732
#> 2023-05-08 20:11:47.596035 INFO::Fitting model to feature number 2371, ASV_696
#> 2023-05-08 20:11:47.599614 INFO::Fitting model to feature number 2372, ASV_2858
#> 2023-05-08 20:11:47.602749 INFO::Fitting model to feature number 2373, ASV_807
#> 2023-05-08 20:11:47.606672 INFO::Fitting model to feature number 2374, ASV_940
#> 2023-05-08 20:11:47.610388 INFO::Fitting model to feature number 2375, ASV_2423
#> 2023-05-08 20:11:47.614151 INFO::Fitting model to feature number 2376, ASV_2446
#> 2023-05-08 20:11:47.617667 INFO::Fitting model to feature number 2377, ASV_2624
#> 2023-05-08 20:11:47.621268 INFO::Fitting model to feature number 2378, ASV_1933
#> 2023-05-08 20:11:47.624677 INFO::Fitting model to feature number 2379, ASV_2831
#> 2023-05-08 20:11:47.628096 INFO::Fitting model to feature number 2380, ASV_553
#> 2023-05-08 20:11:47.631466 INFO::Fitting model to feature number 2381, ASV_1191
#> 2023-05-08 20:11:47.634932 INFO::Fitting model to feature number 2382, ASV_977
#> 2023-05-08 20:11:47.63838 INFO::Fitting model to feature number 2383, ASV_2893
#> 2023-05-08 20:11:47.641703 INFO::Fitting model to feature number 2384, ASV_2466
#> 2023-05-08 20:11:47.645008 INFO::Fitting model to feature number 2385, ASV_872
#> 2023-05-08 20:11:47.648231 INFO::Fitting model to feature number 2386, ASV_1273
#> 2023-05-08 20:11:47.651497 INFO::Fitting model to feature number 2387, ASV_878
#> 2023-05-08 20:11:47.655018 INFO::Fitting model to feature number 2388, ASV_1488
#> 2023-05-08 20:11:47.658371 INFO::Fitting model to feature number 2389, ASV_565
#> 2023-05-08 20:11:47.661414 INFO::Fitting model to feature number 2390, ASV_1715
#> 2023-05-08 20:11:47.664506 INFO::Fitting model to feature number 2391, ASV_2892
#> 2023-05-08 20:11:47.667999 INFO::Fitting model to feature number 2392, ASV_2045
#> 2023-05-08 20:11:47.671319 INFO::Fitting model to feature number 2393, ASV_2851
#> 2023-05-08 20:11:47.674646 INFO::Fitting model to feature number 2394, ASV_1114
#> 2023-05-08 20:11:47.677779 INFO::Fitting model to feature number 2395, ASV_1235
#> 2023-05-08 20:11:47.680887 INFO::Fitting model to feature number 2396, ASV_2260
#> 2023-05-08 20:11:47.685237 INFO::Fitting model to feature number 2397, ASV_1838
#> 2023-05-08 20:11:47.688628 INFO::Fitting model to feature number 2398, ASV_2813
#> 2023-05-08 20:11:47.691819 INFO::Fitting model to feature number 2399, ASV_1595
#> 2023-05-08 20:11:47.695027 INFO::Fitting model to feature number 2400, ASV_2891
#> 2023-05-08 20:11:47.698383 INFO::Fitting model to feature number 2401, ASV_509
#> 2023-05-08 20:11:47.701707 INFO::Fitting model to feature number 2402, ASV_2143
#> 2023-05-08 20:11:47.704927 INFO::Fitting model to feature number 2403, ASV_951
#> 2023-05-08 20:11:47.708258 INFO::Fitting model to feature number 2404, ASV_2059
#> 2023-05-08 20:11:47.711689 INFO::Fitting model to feature number 2405, ASV_1607
#> 2023-05-08 20:11:47.715463 INFO::Fitting model to feature number 2406, ASV_2314
#> 2023-05-08 20:11:47.718901 INFO::Fitting model to feature number 2407, ASV_2139
#> 2023-05-08 20:11:47.722321 INFO::Fitting model to feature number 2408, ASV_2676
#> 2023-05-08 20:11:47.725587 INFO::Fitting model to feature number 2409, ASV_2510
#> 2023-05-08 20:11:47.728972 INFO::Fitting model to feature number 2410, ASV_670
#> 2023-05-08 20:11:47.732269 INFO::Fitting model to feature number 2411, ASV_2312
#> 2023-05-08 20:11:47.735505 INFO::Fitting model to feature number 2412, ASV_1211
#> 2023-05-08 20:11:47.738707 INFO::Fitting model to feature number 2413, ASV_1370
#> 2023-05-08 20:11:47.741846 INFO::Fitting model to feature number 2414, ASV_2603
#> 2023-05-08 20:11:47.745157 INFO::Fitting model to feature number 2415, ASV_1965
#> 2023-05-08 20:11:47.748362 INFO::Fitting model to feature number 2416, ASV_1973
#> 2023-05-08 20:11:47.751584 INFO::Fitting model to feature number 2417, ASV_2907
#> 2023-05-08 20:11:47.754612 INFO::Fitting model to feature number 2418, ASV_1704
#> 2023-05-08 20:11:47.757626 INFO::Fitting model to feature number 2419, ASV_1796
#> 2023-05-08 20:11:47.761543 INFO::Fitting model to feature number 2420, ASV_1840
#> 2023-05-08 20:11:47.764981 INFO::Fitting model to feature number 2421, ASV_2555
#> 2023-05-08 20:11:47.768191 INFO::Fitting model to feature number 2422, ASV_2724
#> 2023-05-08 20:11:47.771368 INFO::Fitting model to feature number 2423, ASV_1795
#> 2023-05-08 20:11:47.77446 INFO::Fitting model to feature number 2424, ASV_982
#> 2023-05-08 20:11:47.77832 INFO::Fitting model to feature number 2425, ASV_1484
#> 2023-05-08 20:11:47.781601 INFO::Fitting model to feature number 2426, ASV_1559
#> 2023-05-08 20:11:47.784817 INFO::Fitting model to feature number 2427, ASV_2733
#> 2023-05-08 20:11:47.788474 INFO::Fitting model to feature number 2428, ASV_2053
#> 2023-05-08 20:11:47.792374 INFO::Fitting model to feature number 2429, ASV_2602
#> 2023-05-08 20:11:47.796003 INFO::Fitting model to feature number 2430, ASV_518
#> 2023-05-08 20:11:47.799297 INFO::Fitting model to feature number 2431, ASV_1902
#> 2023-05-08 20:11:47.802941 INFO::Fitting model to feature number 2432, ASV_1901
#> 2023-05-08 20:11:47.80618 INFO::Fitting model to feature number 2433, ASV_2750
#> 2023-05-08 20:11:47.809404 INFO::Fitting model to feature number 2434, ASV_2752
#> 2023-05-08 20:11:47.812558 INFO::Fitting model to feature number 2435, ASV_2384
#> 2023-05-08 20:11:47.815653 INFO::Fitting model to feature number 2436, ASV_2852
#> 2023-05-08 20:11:47.818882 INFO::Fitting model to feature number 2437, ASV_532
#> 2023-05-08 20:11:47.822414 INFO::Fitting model to feature number 2438, ASV_1962
#> 2023-05-08 20:11:47.826093 INFO::Fitting model to feature number 2439, ASV_788
#> 2023-05-08 20:11:47.829499 INFO::Fitting model to feature number 2440, ASV_2126
#> 2023-05-08 20:11:47.832962 INFO::Fitting model to feature number 2441, ASV_851
#> 2023-05-08 20:11:47.836298 INFO::Fitting model to feature number 2442, ASV_1011
#> 2023-05-08 20:11:47.839992 INFO::Fitting model to feature number 2443, ASV_1788
#> 2023-05-08 20:11:47.843391 INFO::Fitting model to feature number 2444, ASV_1245
#> 2023-05-08 20:11:47.846632 INFO::Fitting model to feature number 2445, ASV_1525
#> 2023-05-08 20:11:47.849818 INFO::Fitting model to feature number 2446, ASV_991
#> 2023-05-08 20:11:47.853191 INFO::Fitting model to feature number 2447, ASV_1098
#> 2023-05-08 20:11:47.856449 INFO::Fitting model to feature number 2448, ASV_1319
#> 2023-05-08 20:11:47.859874 INFO::Fitting model to feature number 2449, ASV_2837
#> 2023-05-08 20:11:47.863161 INFO::Fitting model to feature number 2450, ASV_2862
#> 2023-05-08 20:11:47.866531 INFO::Fitting model to feature number 2451, ASV_2183
#> 2023-05-08 20:11:47.870082 INFO::Fitting model to feature number 2452, ASV_2203
#> 2023-05-08 20:11:47.873416 INFO::Fitting model to feature number 2453, ASV_571
#> 2023-05-08 20:11:47.876758 INFO::Fitting model to feature number 2454, ASV_949
#> 2023-05-08 20:11:47.879984 INFO::Fitting model to feature number 2455, ASV_785
#> 2023-05-08 20:11:47.883161 INFO::Fitting model to feature number 2456, ASV_544
#> 2023-05-08 20:11:47.886539 INFO::Fitting model to feature number 2457, ASV_1099
#> 2023-05-08 20:11:47.889773 INFO::Fitting model to feature number 2458, ASV_2153
#> 2023-05-08 20:11:47.893415 INFO::Fitting model to feature number 2459, ASV_1080
#> 2023-05-08 20:11:47.896947 INFO::Fitting model to feature number 2460, ASV_2684
#> 2023-05-08 20:11:47.900745 INFO::Fitting model to feature number 2461, ASV_601
#> 2023-05-08 20:11:47.904648 INFO::Fitting model to feature number 2462, ASV_1780
#> 2023-05-08 20:11:47.908304 INFO::Fitting model to feature number 2463, ASV_2680
#> 2023-05-08 20:11:47.911653 INFO::Fitting model to feature number 2464, ASV_1142
#> 2023-05-08 20:11:47.915024 INFO::Fitting model to feature number 2465, ASV_2653
#> 2023-05-08 20:11:47.918612 INFO::Fitting model to feature number 2466, ASV_568
#> 2023-05-08 20:11:47.921933 INFO::Fitting model to feature number 2467, ASV_1854
#> 2023-05-08 20:11:47.925148 INFO::Fitting model to feature number 2468, ASV_2914
#> 2023-05-08 20:11:47.928344 INFO::Fitting model to feature number 2469, ASV_2736
#> 2023-05-08 20:11:47.931512 INFO::Fitting model to feature number 2470, ASV_569
#> 2023-05-08 20:11:47.934886 INFO::Fitting model to feature number 2471, ASV_1610
#> 2023-05-08 20:11:47.938107 INFO::Fitting model to feature number 2472, ASV_990
#> 2023-05-08 20:11:47.941304 INFO::Fitting model to feature number 2473, ASV_2221
#> 2023-05-08 20:11:47.944416 INFO::Fitting model to feature number 2474, ASV_2428
#> 2023-05-08 20:11:47.947529 INFO::Fitting model to feature number 2475, ASV_641
#> 2023-05-08 20:11:47.950809 INFO::Fitting model to feature number 2476, ASV_1160
#> 2023-05-08 20:11:47.953938 INFO::Fitting model to feature number 2477, ASV_802
#> 2023-05-08 20:11:47.957433 INFO::Fitting model to feature number 2478, ASV_690
#> 2023-05-08 20:11:47.960718 INFO::Fitting model to feature number 2479, ASV_2573
#> 2023-05-08 20:11:47.96406 INFO::Fitting model to feature number 2480, ASV_1949
#> 2023-05-08 20:11:47.96724 INFO::Fitting model to feature number 2481, ASV_2352
#> 2023-05-08 20:11:47.970324 INFO::Fitting model to feature number 2482, ASV_2761
#> 2023-05-08 20:11:47.973386 INFO::Fitting model to feature number 2483, ASV_1360
#> 2023-05-08 20:11:47.976364 INFO::Fitting model to feature number 2484, ASV_2604
#> 2023-05-08 20:11:47.979339 INFO::Fitting model to feature number 2485, ASV_2882
#> 2023-05-08 20:11:47.983494 INFO::Fitting model to feature number 2486, ASV_2606
#> 2023-05-08 20:11:47.986788 INFO::Fitting model to feature number 2487, ASV_791
#> 2023-05-08 20:11:47.989921 INFO::Fitting model to feature number 2488, ASV_837
#> 2023-05-08 20:11:47.993059 INFO::Fitting model to feature number 2489, ASV_1300
#> 2023-05-08 20:11:47.99617 INFO::Fitting model to feature number 2490, ASV_1675
#> 2023-05-08 20:11:47.999217 INFO::Fitting model to feature number 2491, ASV_2318
#> 2023-05-08 20:11:48.002406 INFO::Fitting model to feature number 2492, ASV_2860
#> 2023-05-08 20:11:48.005724 INFO::Fitting model to feature number 2493, ASV_730
#> 2023-05-08 20:11:48.008914 INFO::Fitting model to feature number 2494, ASV_776
#> 2023-05-08 20:11:48.012229 INFO::Fitting model to feature number 2495, ASV_1209
#> 2023-05-08 20:11:48.015487 INFO::Fitting model to feature number 2496, ASV_733
#> 2023-05-08 20:11:48.019055 INFO::Fitting model to feature number 2497, ASV_1822
#> 2023-05-08 20:11:48.022864 INFO::Fitting model to feature number 2498, ASV_2164
#> 2023-05-08 20:11:48.026062 INFO::Fitting model to feature number 2499, ASV_1210
#> 2023-05-08 20:11:48.030505 INFO::Fitting model to feature number 2500, ASV_755
#> 2023-05-08 20:11:48.034115 INFO::Fitting model to feature number 2501, ASV_2146
#> 2023-05-08 20:11:48.037385 INFO::Fitting model to feature number 2502, ASV_2149
#> 2023-05-08 20:11:48.040569 INFO::Fitting model to feature number 2503, ASV_2207
#> 2023-05-08 20:11:48.043856 INFO::Fitting model to feature number 2504, ASV_1249
#> 2023-05-08 20:11:48.047165 INFO::Fitting model to feature number 2505, ASV_2575
#> 2023-05-08 20:11:48.050398 INFO::Fitting model to feature number 2506, ASV_1878
#> 2023-05-08 20:11:48.05346 INFO::Fitting model to feature number 2507, ASV_2375
#> 2023-05-08 20:11:48.056455 INFO::Fitting model to feature number 2508, ASV_1276
#> 2023-05-08 20:11:48.060276 INFO::Fitting model to feature number 2509, ASV_2330
#> 2023-05-08 20:11:48.064538 INFO::Fitting model to feature number 2510, ASV_994
#> 2023-05-08 20:11:48.067728 INFO::Fitting model to feature number 2511, ASV_1369
#> 2023-05-08 20:11:48.070915 INFO::Fitting model to feature number 2512, ASV_2245
#> 2023-05-08 20:11:48.074408 INFO::Fitting model to feature number 2513, ASV_1594
#> 2023-05-08 20:11:48.077551 INFO::Fitting model to feature number 2514, ASV_1771
#> 2023-05-08 20:11:48.080635 INFO::Fitting model to feature number 2515, ASV_2148
#> 2023-05-08 20:11:48.084027 INFO::Fitting model to feature number 2516, ASV_2287
#> 2023-05-08 20:11:48.087297 INFO::Fitting model to feature number 2517, ASV_759
#> 2023-05-08 20:11:48.090797 INFO::Fitting model to feature number 2518, ASV_907
#> 2023-05-08 20:11:48.094214 INFO::Fitting model to feature number 2519, ASV_1448
#> 2023-05-08 20:11:48.097361 INFO::Fitting model to feature number 2520, ASV_1682
#> 2023-05-08 20:11:48.100519 INFO::Fitting model to feature number 2521, ASV_923
#> 2023-05-08 20:11:48.103865 INFO::Fitting model to feature number 2522, ASV_1056
#> 2023-05-08 20:11:48.108652 INFO::Fitting model to feature number 2523, ASV_1201
#> 2023-05-08 20:11:48.11191 INFO::Fitting model to feature number 2524, ASV_1894
#> 2023-05-08 20:11:48.115907 INFO::Fitting model to feature number 2525, ASV_1993
#> 2023-05-08 20:11:48.119137 INFO::Fitting model to feature number 2526, ASV_2079
#> 2023-05-08 20:11:48.123131 INFO::Fitting model to feature number 2527, ASV_2256
#> 2023-05-08 20:11:48.126653 INFO::Fitting model to feature number 2528, ASV_1695
#> 2023-05-08 20:11:48.129889 INFO::Fitting model to feature number 2529, ASV_2408
#> 2023-05-08 20:11:48.132977 INFO::Fitting model to feature number 2530, ASV_2608
#> 2023-05-08 20:11:48.136244 INFO::Fitting model to feature number 2531, ASV_1509
#> 2023-05-08 20:11:48.139363 INFO::Fitting model to feature number 2532, ASV_2054
#> 2023-05-08 20:11:48.142439 INFO::Fitting model to feature number 2533, ASV_2147
#> 2023-05-08 20:11:48.145591 INFO::Fitting model to feature number 2534, ASV_1683
#> 2023-05-08 20:11:48.149018 INFO::Fitting model to feature number 2535, ASV_2072
#> 2023-05-08 20:11:48.152579 INFO::Fitting model to feature number 2536, ASV_2699
#> 2023-05-08 20:11:48.1559 INFO::Fitting model to feature number 2537, ASV_761
#> 2023-05-08 20:11:48.159043 INFO::Fitting model to feature number 2538, ASV_1784
#> 2023-05-08 20:11:48.16211 INFO::Fitting model to feature number 2539, ASV_2374
#> 2023-05-08 20:11:48.165189 INFO::Fitting model to feature number 2540, ASV_2598
#> 2023-05-08 20:11:48.168292 INFO::Fitting model to feature number 2541, ASV_958
#> 2023-05-08 20:11:48.171824 INFO::Fitting model to feature number 2542, ASV_1907
#> 2023-05-08 20:11:48.175243 INFO::Fitting model to feature number 2543, ASV_2305
#> 2023-05-08 20:11:48.178406 INFO::Fitting model to feature number 2544, ASV_1240
#> 2023-05-08 20:11:48.181533 INFO::Fitting model to feature number 2545, ASV_2082
#> 2023-05-08 20:11:48.184855 INFO::Fitting model to feature number 2546, ASV_1996
#> 2023-05-08 20:11:48.188041 INFO::Fitting model to feature number 2547, ASV_2089
#> 2023-05-08 20:11:48.191238 INFO::Fitting model to feature number 2548, ASV_2737
#> 2023-05-08 20:11:48.194354 INFO::Fitting model to feature number 2549, ASV_1402
#> 2023-05-08 20:11:48.197617 INFO::Fitting model to feature number 2550, ASV_1851
#> 2023-05-08 20:11:48.20114 INFO::Fitting model to feature number 2551, ASV_1914
#> 2023-05-08 20:11:48.204623 INFO::Fitting model to feature number 2552, ASV_1731
#> 2023-05-08 20:11:48.20797 INFO::Fitting model to feature number 2553, ASV_2807
#> 2023-05-08 20:11:48.211227 INFO::Fitting model to feature number 2554, ASV_777
#> 2023-05-08 20:11:48.214489 INFO::Fitting model to feature number 2555, ASV_981
#> 2023-05-08 20:11:48.217904 INFO::Fitting model to feature number 2556, ASV_2544
#> 2023-05-08 20:11:48.221059 INFO::Fitting model to feature number 2557, ASV_2740
#> 2023-05-08 20:11:48.224552 INFO::Fitting model to feature number 2558, ASV_1633
#> 2023-05-08 20:11:48.227717 INFO::Fitting model to feature number 2559, ASV_1783
#> 2023-05-08 20:11:48.231356 INFO::Fitting model to feature number 2560, ASV_2254
#> 2023-05-08 20:11:48.234911 INFO::Fitting model to feature number 2561, ASV_787
#> 2023-05-08 20:11:48.238286 INFO::Fitting model to feature number 2562, ASV_1356
#> 2023-05-08 20:11:48.241676 INFO::Fitting model to feature number 2563, ASV_1557
#> 2023-05-08 20:11:48.244802 INFO::Fitting model to feature number 2564, ASV_2159
#> 2023-05-08 20:11:48.248233 INFO::Fitting model to feature number 2565, ASV_2353
#> 2023-05-08 20:11:48.251324 INFO::Fitting model to feature number 2566, ASV_2485
#> 2023-05-08 20:11:48.254323 INFO::Fitting model to feature number 2567, ASV_1520
#> 2023-05-08 20:11:48.257326 INFO::Fitting model to feature number 2568, ASV_2819
#> 2023-05-08 20:11:48.260338 INFO::Fitting model to feature number 2569, ASV_2506
#> 2023-05-08 20:11:48.263785 INFO::Fitting model to feature number 2570, ASV_1192
#> 2023-05-08 20:11:48.267069 INFO::Fitting model to feature number 2571, ASV_2191
#> 2023-05-08 20:11:48.270223 INFO::Fitting model to feature number 2572, ASV_2376
#> 2023-05-08 20:11:48.273338 INFO::Fitting model to feature number 2573, ASV_1473
#> 2023-05-08 20:11:48.276712 INFO::Fitting model to feature number 2574, ASV_1886
#> 2023-05-08 20:11:48.2809 INFO::Fitting model to feature number 2575, ASV_2096
#> 2023-05-08 20:11:48.284192 INFO::Fitting model to feature number 2576, ASV_1542
#> 2023-05-08 20:11:48.287332 INFO::Fitting model to feature number 2577, ASV_1759
#> 2023-05-08 20:11:48.29039 INFO::Fitting model to feature number 2578, ASV_1963
#> 2023-05-08 20:11:48.294064 INFO::Fitting model to feature number 2579, ASV_2389
#> 2023-05-08 20:11:48.297437 INFO::Fitting model to feature number 2580, ASV_2729
#> 2023-05-08 20:11:48.300675 INFO::Fitting model to feature number 2581, ASV_2395
#> 2023-05-08 20:11:48.303808 INFO::Fitting model to feature number 2582, ASV_2515
#> 2023-05-08 20:11:48.306889 INFO::Fitting model to feature number 2583, ASV_2490
#> 2023-05-08 20:11:48.310163 INFO::Fitting model to feature number 2584, ASV_2640
#> 2023-05-08 20:11:48.313368 INFO::Fitting model to feature number 2585, ASV_801
#> 2023-05-08 20:11:48.316606 INFO::Fitting model to feature number 2586, ASV_2566
#> 2023-05-08 20:11:48.319763 INFO::Fitting model to feature number 2587, ASV_2806
#> 2023-05-08 20:11:48.32308 INFO::Fitting model to feature number 2588, ASV_1120
#> 2023-05-08 20:11:48.326548 INFO::Fitting model to feature number 2589, ASV_2137
#> 2023-05-08 20:11:48.329831 INFO::Fitting model to feature number 2590, ASV_1463
#> 2023-05-08 20:11:48.333011 INFO::Fitting model to feature number 2591, ASV_2845
#> 2023-05-08 20:11:48.33609 INFO::Fitting model to feature number 2592, ASV_848
#> 2023-05-08 20:11:48.339242 INFO::Fitting model to feature number 2593, ASV_2129
#> 2023-05-08 20:11:48.34269 INFO::Fitting model to feature number 2594, ASV_1189
#> 2023-05-08 20:11:48.345969 INFO::Fitting model to feature number 2595, ASV_952
#> 2023-05-08 20:11:48.349172 INFO::Fitting model to feature number 2596, ASV_1850
#> 2023-05-08 20:11:48.352306 INFO::Fitting model to feature number 2597, ASV_1263
#> 2023-05-08 20:11:48.355739 INFO::Fitting model to feature number 2598, ASV_2354
#> 2023-05-08 20:11:48.359766 INFO::Fitting model to feature number 2599, ASV_2672
#> 2023-05-08 20:11:48.363238 INFO::Fitting model to feature number 2600, ASV_859
#> 2023-05-08 20:11:48.3665 INFO::Fitting model to feature number 2601, ASV_2018
#> 2023-05-08 20:11:48.3696 INFO::Fitting model to feature number 2602, ASV_1766
#> 2023-05-08 20:11:48.372809 INFO::Fitting model to feature number 2603, ASV_1925
#> 2023-05-08 20:11:48.376058 INFO::Fitting model to feature number 2604, ASV_1939
#> 2023-05-08 20:11:48.379238 INFO::Fitting model to feature number 2605, ASV_1966
#> 2023-05-08 20:11:48.382619 INFO::Fitting model to feature number 2606, ASV_2229
#> 2023-05-08 20:11:48.385856 INFO::Fitting model to feature number 2607, ASV_2890
#> 2023-05-08 20:11:48.394528 INFO::Fitting model to feature number 2608, ASV_1505
#> 2023-05-08 20:11:48.398571 INFO::Fitting model to feature number 2609, ASV_2227
#> 2023-05-08 20:11:48.401894 INFO::Fitting model to feature number 2610, ASV_2702
#> 2023-05-08 20:11:48.405338 INFO::Fitting model to feature number 2611, ASV_2760
#> 2023-05-08 20:11:48.408846 INFO::Fitting model to feature number 2612, ASV_2430
#> 2023-05-08 20:11:48.412168 INFO::Fitting model to feature number 2613, ASV_2521
#> 2023-05-08 20:11:48.415275 INFO::Fitting model to feature number 2614, ASV_2661
#> 2023-05-08 20:11:48.418439 INFO::Fitting model to feature number 2615, ASV_862
#> 2023-05-08 20:11:48.422465 INFO::Fitting model to feature number 2616, ASV_1347
#> 2023-05-08 20:11:48.426062 INFO::Fitting model to feature number 2617, ASV_1746
#> 2023-05-08 20:11:48.429597 INFO::Fitting model to feature number 2618, ASV_2311
#> 2023-05-08 20:11:48.432956 INFO::Fitting model to feature number 2619, ASV_2827
#> 2023-05-08 20:11:48.436284 INFO::Fitting model to feature number 2620, ASV_2472
#> 2023-05-08 20:11:48.43967 INFO::Fitting model to feature number 2621, ASV_1264
#> 2023-05-08 20:11:48.442964 INFO::Fitting model to feature number 2622, ASV_2668
#> 2023-05-08 20:11:48.446452 INFO::Fitting model to feature number 2623, ASV_1915
#> 2023-05-08 20:11:48.449776 INFO::Fitting model to feature number 2624, ASV_882
#> 2023-05-08 20:11:48.453314 INFO::Fitting model to feature number 2625, ASV_2363
#> 2023-05-08 20:11:48.456592 INFO::Fitting model to feature number 2626, ASV_1809
#> 2023-05-08 20:11:48.459853 INFO::Fitting model to feature number 2627, ASV_2505
#> 2023-05-08 20:11:48.463123 INFO::Fitting model to feature number 2628, ASV_2034
#> 2023-05-08 20:11:48.466376 INFO::Fitting model to feature number 2629, ASV_911
#> 2023-05-08 20:11:48.469482 INFO::Fitting model to feature number 2630, ASV_1071
#> 2023-05-08 20:11:48.472498 INFO::Fitting model to feature number 2631, ASV_886
#> 2023-05-08 20:11:48.475568 INFO::Fitting model to feature number 2632, ASV_1590
#> 2023-05-08 20:11:48.478578 INFO::Fitting model to feature number 2633, ASV_890
#> 2023-05-08 20:11:48.481786 INFO::Fitting model to feature number 2634, ASV_2398
#> 2023-05-08 20:11:48.485112 INFO::Fitting model to feature number 2635, ASV_2812
#> 2023-05-08 20:11:48.488354 INFO::Fitting model to feature number 2636, ASV_2518
#> 2023-05-08 20:11:48.491651 INFO::Fitting model to feature number 2637, ASV_2599
#> 2023-05-08 20:11:48.494844 INFO::Fitting model to feature number 2638, ASV_2621
#> 2023-05-08 20:11:48.498625 INFO::Fitting model to feature number 2639, ASV_1842
#> 2023-05-08 20:11:48.502406 INFO::Fitting model to feature number 2640, ASV_2558
#> 2023-05-08 20:11:48.505754 INFO::Fitting model to feature number 2641, ASV_2895
#> 2023-05-08 20:11:48.509218 INFO::Fitting model to feature number 2642, ASV_2462
#> 2023-05-08 20:11:48.512695 INFO::Fitting model to feature number 2643, ASV_2722
#> 2023-05-08 20:11:48.516109 INFO::Fitting model to feature number 2644, ASV_901
#> 2023-05-08 20:11:48.519382 INFO::Fitting model to feature number 2645, ASV_1387
#> 2023-05-08 20:11:48.522455 INFO::Fitting model to feature number 2646, ASV_2464
#> 2023-05-08 20:11:48.525727 INFO::Fitting model to feature number 2647, ASV_2175
#> 2023-05-08 20:11:48.529064 INFO::Fitting model to feature number 2648, ASV_2333
#> 2023-05-08 20:11:48.532579 INFO::Fitting model to feature number 2649, ASV_2655
#> 2023-05-08 20:11:48.535908 INFO::Fitting model to feature number 2650, ASV_919
#> 2023-05-08 20:11:48.539071 INFO::Fitting model to feature number 2651, ASV_2019
#> 2023-05-08 20:11:48.542425 INFO::Fitting model to feature number 2652, ASV_1690
#> 2023-05-08 20:11:48.545812 INFO::Fitting model to feature number 2653, ASV_2303
#> 2023-05-08 20:11:48.549063 INFO::Fitting model to feature number 2654, ASV_1910
#> 2023-05-08 20:11:48.552168 INFO::Fitting model to feature number 2655, ASV_2583
#> 2023-05-08 20:11:48.555192 INFO::Fitting model to feature number 2656, ASV_1496
#> 2023-05-08 20:11:48.558275 INFO::Fitting model to feature number 2657, ASV_2902
#> 2023-05-08 20:11:48.561452 INFO::Fitting model to feature number 2658, ASV_2863
#> 2023-05-08 20:11:48.564572 INFO::Fitting model to feature number 2659, ASV_2488
#> 2023-05-08 20:11:48.567606 INFO::Fitting model to feature number 2660, ASV_1761
#> 2023-05-08 20:11:48.570592 INFO::Fitting model to feature number 2661, ASV_1908
#> 2023-05-08 20:11:48.573603 INFO::Fitting model to feature number 2662, ASV_2327
#> 2023-05-08 20:11:48.576862 INFO::Fitting model to feature number 2663, ASV_2481
#> 2023-05-08 20:11:48.580564 INFO::Fitting model to feature number 2664, ASV_2533
#> 2023-05-08 20:11:48.584072 INFO::Fitting model to feature number 2665, ASV_2563
#> 2023-05-08 20:11:48.587569 INFO::Fitting model to feature number 2666, ASV_2903
#> 2023-05-08 20:11:48.590818 INFO::Fitting model to feature number 2667, ASV_941
#> 2023-05-08 20:11:48.594316 INFO::Fitting model to feature number 2668, ASV_1010
#> 2023-05-08 20:11:48.597668 INFO::Fitting model to feature number 2669, ASV_989
#> 2023-05-08 20:11:48.6011 INFO::Fitting model to feature number 2670, ASV_2014
#> 2023-05-08 20:11:48.604373 INFO::Fitting model to feature number 2671, ASV_1709
#> 2023-05-08 20:11:48.607574 INFO::Fitting model to feature number 2672, ASV_998
#> 2023-05-08 20:11:48.610999 INFO::Fitting model to feature number 2673, ASV_1228
#> 2023-05-08 20:11:48.614222 INFO::Fitting model to feature number 2674, ASV_2475
#> 2023-05-08 20:11:48.617385 INFO::Fitting model to feature number 2675, ASV_2597
#> 2023-05-08 20:11:48.620674 INFO::Fitting model to feature number 2676, ASV_2682
#> 2023-05-08 20:11:48.624004 INFO::Fitting model to feature number 2677, ASV_2360
#> 2023-05-08 20:11:48.627446 INFO::Fitting model to feature number 2678, ASV_1246
#> 2023-05-08 20:11:48.630686 INFO::Fitting model to feature number 2679, ASV_1014
#> 2023-05-08 20:11:48.634098 INFO::Fitting model to feature number 2680, ASV_1867
#> 2023-05-08 20:11:48.637234 INFO::Fitting model to feature number 2681, ASV_1289
#> 2023-05-08 20:11:48.640549 INFO::Fitting model to feature number 2682, ASV_2056
#> 2023-05-08 20:11:48.643785 INFO::Fitting model to feature number 2683, ASV_1022
#> 2023-05-08 20:11:48.646958 INFO::Fitting model to feature number 2684, ASV_2833
#> 2023-05-08 20:11:48.650109 INFO::Fitting model to feature number 2685, ASV_1755
#> 2023-05-08 20:11:48.653212 INFO::Fitting model to feature number 2686, ASV_1457
#> 2023-05-08 20:11:48.657428 INFO::Fitting model to feature number 2687, ASV_1945
#> 2023-05-08 20:11:48.661074 INFO::Fitting model to feature number 2688, ASV_1687
#> 2023-05-08 20:11:48.664394 INFO::Fitting model to feature number 2689, ASV_1643
#> 2023-05-08 20:11:48.667532 INFO::Fitting model to feature number 2690, ASV_1140
#> 2023-05-08 20:11:48.67072 INFO::Fitting model to feature number 2691, ASV_1424
#> 2023-05-08 20:11:48.674051 INFO::Fitting model to feature number 2692, ASV_1641
#> 2023-05-08 20:11:48.677655 INFO::Fitting model to feature number 2693, ASV_1826
#> 2023-05-08 20:11:48.681195 INFO::Fitting model to feature number 2694, ASV_1364
#> 2023-05-08 20:11:48.684731 INFO::Fitting model to feature number 2695, ASV_1503
#> 2023-05-08 20:11:48.688223 INFO::Fitting model to feature number 2696, ASV_1032
#> 2023-05-08 20:11:48.691574 INFO::Fitting model to feature number 2697, ASV_2401
#> 2023-05-08 20:11:48.694767 INFO::Fitting model to feature number 2698, ASV_2051
#> 2023-05-08 20:11:48.697931 INFO::Fitting model to feature number 2699, ASV_2104
#> 2023-05-08 20:11:48.701009 INFO::Fitting model to feature number 2700, ASV_1190
#> 2023-05-08 20:11:48.704218 INFO::Fitting model to feature number 2701, ASV_2317
#> 2023-05-08 20:11:48.707785 INFO::Fitting model to feature number 2702, ASV_2532
#> 2023-05-08 20:11:48.710822 INFO::Fitting model to feature number 2703, ASV_1118
#> 2023-05-08 20:11:48.714451 INFO::Fitting model to feature number 2704, ASV_1439
#> 2023-05-08 20:11:48.718277 INFO::Fitting model to feature number 2705, ASV_2529
#> 2023-05-08 20:11:48.722305 INFO::Fitting model to feature number 2706, ASV_2406
#> 2023-05-08 20:11:48.726032 INFO::Fitting model to feature number 2707, ASV_1125
#> 2023-05-08 20:11:48.729384 INFO::Fitting model to feature number 2708, ASV_2300
#> 2023-05-08 20:11:48.733194 INFO::Fitting model to feature number 2709, ASV_1298
#> 2023-05-08 20:11:48.737029 INFO::Fitting model to feature number 2710, ASV_2660
#> 2023-05-08 20:11:48.740418 INFO::Fitting model to feature number 2711, ASV_2112
#> 2023-05-08 20:11:48.743901 INFO::Fitting model to feature number 2712, ASV_2098
#> 2023-05-08 20:11:48.747326 INFO::Fitting model to feature number 2713, ASV_2773
#> 2023-05-08 20:11:48.750743 INFO::Fitting model to feature number 2714, ASV_2124
#> 2023-05-08 20:11:48.754162 INFO::Fitting model to feature number 2715, ASV_2641
#> 2023-05-08 20:11:48.757468 INFO::Fitting model to feature number 2716, ASV_2163
#> 2023-05-08 20:11:48.760713 INFO::Fitting model to feature number 2717, ASV_1146
#> 2023-05-08 20:11:48.763963 INFO::Fitting model to feature number 2718, ASV_2696
#> 2023-05-08 20:11:48.767253 INFO::Fitting model to feature number 2719, ASV_1818
#> 2023-05-08 20:11:48.770449 INFO::Fitting model to feature number 2720, ASV_2764
#> 2023-05-08 20:11:48.773748 INFO::Fitting model to feature number 2721, ASV_2695
#> 2023-05-08 20:11:48.77715 INFO::Fitting model to feature number 2722, ASV_1556
#> 2023-05-08 20:11:48.781141 INFO::Fitting model to feature number 2723, ASV_1941
#> 2023-05-08 20:11:48.784584 INFO::Fitting model to feature number 2724, ASV_2077
#> 2023-05-08 20:11:48.787913 INFO::Fitting model to feature number 2725, ASV_2102
#> 2023-05-08 20:11:48.791168 INFO::Fitting model to feature number 2726, ASV_2377
#> 2023-05-08 20:11:48.794365 INFO::Fitting model to feature number 2727, ASV_2675
#> 2023-05-08 20:11:48.79779 INFO::Fitting model to feature number 2728, ASV_2754
#> 2023-05-08 20:11:48.801077 INFO::Fitting model to feature number 2729, ASV_1159
#> 2023-05-08 20:11:48.804373 INFO::Fitting model to feature number 2730, ASV_2278
#> 2023-05-08 20:11:48.807867 INFO::Fitting model to feature number 2731, ASV_2757
#> 2023-05-08 20:11:48.811445 INFO::Fitting model to feature number 2732, ASV_1195
#> 2023-05-08 20:11:48.815022 INFO::Fitting model to feature number 2733, ASV_1394
#> 2023-05-08 20:11:48.818411 INFO::Fitting model to feature number 2734, ASV_1864
#> 2023-05-08 20:11:48.8217 INFO::Fitting model to feature number 2735, ASV_2625
#> 2023-05-08 20:11:48.82488 INFO::Fitting model to feature number 2736, ASV_2639
#> 2023-05-08 20:11:48.828522 INFO::Fitting model to feature number 2737, ASV_2588
#> 2023-05-08 20:11:48.831888 INFO::Fitting model to feature number 2738, ASV_2889
#> 2023-05-08 20:11:48.83507 INFO::Fitting model to feature number 2739, ASV_2753
#> 2023-05-08 20:11:48.838255 INFO::Fitting model to feature number 2740, ASV_1169
#> 2023-05-08 20:11:48.84165 INFO::Fitting model to feature number 2741, ASV_1722
#> 2023-05-08 20:11:48.845631 INFO::Fitting model to feature number 2742, ASV_1172
#> 2023-05-08 20:11:48.849081 INFO::Fitting model to feature number 2743, ASV_1199
#> 2023-05-08 20:11:48.852308 INFO::Fitting model to feature number 2744, ASV_1440
#> 2023-05-08 20:11:48.855491 INFO::Fitting model to feature number 2745, ASV_2210
#> 2023-05-08 20:11:48.858826 INFO::Fitting model to feature number 2746, ASV_2883
#> 2023-05-08 20:11:48.862075 INFO::Fitting model to feature number 2747, ASV_1178
#> 2023-05-08 20:11:48.865181 INFO::Fitting model to feature number 2748, ASV_2572
#> 2023-05-08 20:11:48.868245 INFO::Fitting model to feature number 2749, ASV_1194
#> 2023-05-08 20:11:48.872073 INFO::Fitting model to feature number 2750, ASV_1571
#> 2023-05-08 20:11:48.875812 INFO::Fitting model to feature number 2751, ASV_2698
#> 2023-05-08 20:11:48.879433 INFO::Fitting model to feature number 2752, ASV_2161
#> 2023-05-08 20:11:48.882696 INFO::Fitting model to feature number 2753, ASV_2516
#> 2023-05-08 20:11:48.885815 INFO::Fitting model to feature number 2754, ASV_2196
#> 2023-05-08 20:11:48.888896 INFO::Fitting model to feature number 2755, ASV_2534
#> 2023-05-08 20:11:48.892655 INFO::Fitting model to feature number 2756, ASV_1233
#> 2023-05-08 20:11:48.896018 INFO::Fitting model to feature number 2757, ASV_1940
#> 2023-05-08 20:11:48.899333 INFO::Fitting model to feature number 2758, ASV_2228
#> 2023-05-08 20:11:48.902653 INFO::Fitting model to feature number 2759, ASV_2742
#> 2023-05-08 20:11:48.90636 INFO::Fitting model to feature number 2760, ASV_1399
#> 2023-05-08 20:11:48.909967 INFO::Fitting model to feature number 2761, ASV_2342
#> 2023-05-08 20:11:48.91336 INFO::Fitting model to feature number 2762, ASV_2748
#> 2023-05-08 20:11:48.916735 INFO::Fitting model to feature number 2763, ASV_2768
#> 2023-05-08 20:11:48.920086 INFO::Fitting model to feature number 2764, ASV_1274
#> 2023-05-08 20:11:48.923601 INFO::Fitting model to feature number 2765, ASV_2482
#> 2023-05-08 20:11:48.92701 INFO::Fitting model to feature number 2766, ASV_1961
#> 2023-05-08 20:11:48.930371 INFO::Fitting model to feature number 2767, ASV_2438
#> 2023-05-08 20:11:48.933674 INFO::Fitting model to feature number 2768, ASV_2186
#> 2023-05-08 20:11:48.937167 INFO::Fitting model to feature number 2769, ASV_2870
#> 2023-05-08 20:11:48.940628 INFO::Fitting model to feature number 2770, ASV_2002
#> 2023-05-08 20:11:48.943913 INFO::Fitting model to feature number 2771, ASV_2271
#> 2023-05-08 20:11:48.947148 INFO::Fitting model to feature number 2772, ASV_1279
#> 2023-05-08 20:11:48.950228 INFO::Fitting model to feature number 2773, ASV_1453
#> 2023-05-08 20:11:48.953739 INFO::Fitting model to feature number 2774, ASV_2033
#> 2023-05-08 20:11:48.957055 INFO::Fitting model to feature number 2775, ASV_2253
#> 2023-05-08 20:11:48.960313 INFO::Fitting model to feature number 2776, ASV_2861
#> 2023-05-08 20:11:48.963537 INFO::Fitting model to feature number 2777, ASV_2359
#> 2023-05-08 20:11:48.966832 INFO::Fitting model to feature number 2778, ASV_2234
#> 2023-05-08 20:11:48.970086 INFO::Fitting model to feature number 2779, ASV_1304
#> 2023-05-08 20:11:48.97323 INFO::Fitting model to feature number 2780, ASV_1767
#> 2023-05-08 20:11:48.976539 INFO::Fitting model to feature number 2781, ASV_2261
#> 2023-05-08 20:11:48.979673 INFO::Fitting model to feature number 2782, ASV_2281
#> 2023-05-08 20:11:48.982815 INFO::Fitting model to feature number 2783, ASV_2652
#> 2023-05-08 20:11:48.986113 INFO::Fitting model to feature number 2784, ASV_2557
#> 2023-05-08 20:11:48.989218 INFO::Fitting model to feature number 2785, ASV_1723
#> 2023-05-08 20:11:48.992314 INFO::Fitting model to feature number 2786, ASV_2128
#> 2023-05-08 20:11:48.995335 INFO::Fitting model to feature number 2787, ASV_2686
#> 2023-05-08 20:11:48.99844 INFO::Fitting model to feature number 2788, ASV_2822
#> 2023-05-08 20:11:49.00213 INFO::Fitting model to feature number 2789, ASV_1673
#> 2023-05-08 20:11:49.005422 INFO::Fitting model to feature number 2790, ASV_2396
#> 2023-05-08 20:11:49.008491 INFO::Fitting model to feature number 2791, ASV_2266
#> 2023-05-08 20:11:49.011487 INFO::Fitting model to feature number 2792, ASV_1471
#> 2023-05-08 20:11:49.014438 INFO::Fitting model to feature number 2793, ASV_2711
#> 2023-05-08 20:11:49.017818 INFO::Fitting model to feature number 2794, ASV_1728
#> 2023-05-08 20:11:49.021729 INFO::Fitting model to feature number 2795, ASV_2663
#> 2023-05-08 20:11:49.025301 INFO::Fitting model to feature number 2796, ASV_2499
#> 2023-05-08 20:11:49.029036 INFO::Fitting model to feature number 2797, ASV_2172
#> 2023-05-08 20:11:49.033231 INFO::Fitting model to feature number 2798, ASV_1492
#> 2023-05-08 20:11:49.036788 INFO::Fitting model to feature number 2799, ASV_2009
#> 2023-05-08 20:11:49.040388 INFO::Fitting model to feature number 2800, ASV_2871
#> 2023-05-08 20:11:49.043696 INFO::Fitting model to feature number 2801, ASV_1554
#> 2023-05-08 20:11:49.047121 INFO::Fitting model to feature number 2802, ASV_1737
#> 2023-05-08 20:11:49.050543 INFO::Fitting model to feature number 2803, ASV_1762
#> 2023-05-08 20:11:49.054002 INFO::Fitting model to feature number 2804, ASV_2457
#> 2023-05-08 20:11:49.057295 INFO::Fitting model to feature number 2805, ASV_2487
#> 2023-05-08 20:11:49.060512 INFO::Fitting model to feature number 2806, ASV_2097
#> 2023-05-08 20:11:49.063848 INFO::Fitting model to feature number 2807, ASV_1646
#> 2023-05-08 20:11:49.06709 INFO::Fitting model to feature number 2808, ASV_2898
#> 2023-05-08 20:11:49.070377 INFO::Fitting model to feature number 2809, ASV_1656
#> 2023-05-08 20:11:49.073614 INFO::Fitting model to feature number 2810, ASV_2174
#> 2023-05-08 20:11:49.07705 INFO::Fitting model to feature number 2811, ASV_1678
#> 2023-05-08 20:11:49.080483 INFO::Fitting model to feature number 2812, ASV_1786
#> 2023-05-08 20:11:49.083733 INFO::Fitting model to feature number 2813, ASV_2636
#> 2023-05-08 20:11:49.086886 INFO::Fitting model to feature number 2814, ASV_1839
#> 2023-05-08 20:11:49.090084 INFO::Fitting model to feature number 2815, ASV_1823
#> 2023-05-08 20:11:49.093196 INFO::Fitting model to feature number 2816, ASV_2291
#> 2023-05-08 20:11:49.096707 INFO::Fitting model to feature number 2817, ASV_2216
#> 2023-05-08 20:11:49.099921 INFO::Fitting model to feature number 2818, ASV_1893
#> 2023-05-08 20:11:49.103117 INFO::Fitting model to feature number 2819, ASV_2610
#> 2023-05-08 20:11:49.106209 INFO::Fitting model to feature number 2820, ASV_2865
#> 2023-05-08 20:11:49.109239 INFO::Fitting model to feature number 2821, ASV_2037
#> 2023-05-08 20:11:49.113183 INFO::Fitting model to feature number 2822, ASV_2168
#> 2023-05-08 20:11:49.11654 INFO::Fitting model to feature number 2823, ASV_2593
#> 2023-05-08 20:11:49.119881 INFO::Fitting model to feature number 2824, ASV_2838
#> 2023-05-08 20:11:49.123268 INFO::Fitting model to feature number 2825, ASV_2081
#> 2023-05-08 20:11:49.126858 INFO::Fitting model to feature number 2826, ASV_2441
#> 2023-05-08 20:11:49.130168 INFO::Fitting model to feature number 2827, ASV_2180
#> 2023-05-08 20:11:49.133338 INFO::Fitting model to feature number 2828, ASV_2545
#> 2023-05-08 20:11:49.136411 INFO::Fitting model to feature number 2829, ASV_2085
#> 2023-05-08 20:11:49.139474 INFO::Fitting model to feature number 2830, ASV_2741
#> 2023-05-08 20:11:49.142829 INFO::Fitting model to feature number 2831, ASV_2240
#> 2023-05-08 20:11:49.146234 INFO::Fitting model to feature number 2832, ASV_2320
#> 2023-05-08 20:11:49.149909 INFO::Fitting model to feature number 2833, ASV_2177
#> 2023-05-08 20:11:49.153086 INFO::Fitting model to feature number 2834, ASV_2239
#> 2023-05-08 20:11:49.15625 INFO::Fitting model to feature number 2835, ASV_2576
#> 2023-05-08 20:11:49.159503 INFO::Fitting model to feature number 2836, ASV_2834
#> 2023-05-08 20:11:49.162605 INFO::Fitting model to feature number 2837, ASV_2703
#> 2023-05-08 20:11:49.165891 INFO::Fitting model to feature number 2838, ASV_2454
#> 2023-05-08 20:11:49.169209 INFO::Fitting model to feature number 2839, ASV_2887
#> 2023-05-08 20:11:49.172767 INFO::Fitting model to feature number 2840, ASV_2231
#> 2023-05-08 20:11:49.176365 INFO::Fitting model to feature number 2841, ASV_2803
#> 2023-05-08 20:11:49.377836 INFO::Counting total values for each feature
#> 2023-05-08 20:11:49.532942 WARNING::Deleting existing residuals file: ./2.Microbial community analysis/Maaslin2/residuals.rds
#> 2023-05-08 20:11:49.535118 INFO::Writing residuals to file ./2.Microbial community analysis/Maaslin2/residuals.rds
#> 2023-05-08 20:11:49.551629 WARNING::Deleting existing fitted file: ./2.Microbial community analysis/Maaslin2/fitted.rds
#> 2023-05-08 20:11:49.553713 INFO::Writing fitted values to file ./2.Microbial community analysis/Maaslin2/fitted.rds
#> 2023-05-08 20:11:49.559917 INFO::Writing all results to file (ordered by increasing q-values): ./2.Microbial community analysis/Maaslin2/all_results.tsv
#> 2023-05-08 20:11:49.606275 INFO::Writing the significant results (those which are less than or equal to the threshold of 0.250000 ) to file (ordered by increasing q-values): ./2.Microbial community analysis/Maaslin2/significant_results.tsv
dat = fit_data$results
head(dat)tab.d9 = dat %>%
# rownames_to_column(var = "id") %>%
dplyr::select(feature,qval) %>%
dplyr::filter(qval < 0.05) %>%
dplyr::rename(
OTU = feature,
p = qval
) %>%
dplyr::mutate(group = "Maaslin2")
head(tab.d9)Code 4L（Example 27）
# BiocManager::install("exactRankTests")
library(exactRankTests)
library(nlme)
library(dplyr)
library(ggplot2)
library(compositions)
# OTU table should be a matrix/data.frame with each feature in rows and sample in columns.
# Metadata should be a matrix/data.frame containing the sample identifier.
# Data Pre-Processing
feature_table_pre_process = function(feature_table, meta_data, sample_var, group_var = NULL,
out_cut = 0.05, zero_cut = 0.90, lib_cut, neg_lb){
feature_table = data.frame(feature_table, check.names = FALSE)
meta_data = data.frame(meta_data, check.names = FALSE)
# Drop unused levels
meta_data[] = lapply(meta_data, function(x) if(is.factor(x)) factor(x) else x)
# Match sample IDs between metadata and feature table
sample_ID = intersect(meta_data[, sample_var], colnames(feature_table))
feature_table = feature_table[, sample_ID]
meta_data = meta_data[match(sample_ID, meta_data[, sample_var]), ]
# 1. Identify outliers within each taxon
if (!is.null(group_var)) {
group = meta_data[, group_var]
z = feature_table + 1 # Add pseudo-count (1)
f = log(z); f[f == 0] = NA; f = colMeans(f, na.rm = T)
f_fit = lm(f ~ group)
e = rep(0, length(f)); e[!is.na(group)] = residuals(f_fit)
y = t(t(z) - e)
outlier_check = function(x){
# Fitting the mixture model using the algorithm of Peddada, S. Das, and JT Gene Hwang (2002)
mu1 = quantile(x, 0.25, na.rm = T); mu2 = quantile(x, 0.75, na.rm = T)
sigma1 = quantile(x, 0.75, na.rm = T) - quantile(x, 0.25, na.rm = T); sigma2 = sigma1
pi = 0.75
n = length(x)
epsilon = 100
tol = 1e-5
score = pi*dnorm(x, mean = mu1, sd = sigma1)/((1 - pi)*dnorm(x, mean = mu2, sd = sigma2))
while (epsilon > tol) {
grp1_ind = (score >= 1)
mu1_new = mean(x[grp1_ind]); mu2_new = mean(x[!grp1_ind])
sigma1_new = sd(x[grp1_ind]); if(is.na(sigma1_new)) sigma1_new = 0
sigma2_new = sd(x[!grp1_ind]); if(is.na(sigma2_new)) sigma2_new = 0
pi_new = sum(grp1_ind)/n
para = c(mu1_new, mu2_new, sigma1_new, sigma2_new, pi_new)
if(any(is.na(para))) break
score = pi_new * dnorm(x, mean = mu1_new, sd = sigma1_new)/
((1-pi_new) * dnorm(x, mean = mu2_new, sd = sigma2_new))
epsilon = sqrt((mu1 - mu1_new)^2 + (mu2 - mu2_new)^2 +
(sigma1 - sigma1_new)^2 + (sigma2 - sigma2_new)^2 + (pi - pi_new)^2)
mu1 = mu1_new; mu2 = mu2_new; sigma1 = sigma1_new; sigma2 = sigma2_new; pi = pi_new
}
if(mu1 + 1.96 * sigma1 < mu2 - 1.96 * sigma2){
if(pi < out_cut){
out_ind = grp1_ind
}else if(pi > 1 - out_cut){
out_ind = (!grp1_ind)
}else{
out_ind = rep(FALSE, n)
}
}else{
out_ind = rep(FALSE, n)
}
return(out_ind)
}
out_ind = matrix(FALSE, nrow = nrow(feature_table), ncol = ncol(feature_table))
out_ind[, !is.na(group)] = t(apply(y, 1, function(i)
unlist(tapply(i, group, function(j) outlier_check(j)))))
feature_table[out_ind] = NA
}
# 2. Discard taxa with zeros >= zero_cut
zero_prop = apply(feature_table, 1, function(x) sum(x == 0, na.rm = TRUE)/length(x[!is.na(x)]))
taxa_del = which(zero_prop >= zero_cut)
if(length(taxa_del) > 0){
feature_table = feature_table[- taxa_del, ]
}
# 3. Discard samples with library size < lib_cut
lib_size = colSums(feature_table, na.rm = TRUE)
if(any(lib_size < lib_cut)){
subj_del = which(lib_size < lib_cut)
feature_table = feature_table[, - subj_del]
meta_data = meta_data[- subj_del, ]
}
# 4. Identify taxa with structure zeros
if (!is.null(group_var)) {
group = factor(meta_data[, group_var])
present_table = as.matrix(feature_table)
present_table[is.na(present_table)] = 0
present_table[present_table != 0] = 1
p_hat = t(apply(present_table, 1, function(x)
unlist(tapply(x, group, function(y) mean(y, na.rm = T)))))
samp_size = t(apply(feature_table, 1, function(x)
unlist(tapply(x, group, function(y) length(y[!is.na(y)])))))
p_hat_lo = p_hat - 1.96 * sqrt(p_hat * (1 - p_hat)/samp_size)
struc_zero = (p_hat == 0) * 1
# Whether we need to classify a taxon into structural zero by its negative lower bound?
if(neg_lb) struc_zero[p_hat_lo <= 0] = 1
# Entries considered to be structural zeros are set to be 0s
struc_ind = struc_zero[, group]
feature_table = feature_table * (1 - struc_ind)
colnames(struc_zero) = paste0("structural_zero (", colnames(struc_zero), ")")
}else{
struc_zero = NULL
}
# 5. Return results
res = list(feature_table = feature_table, meta_data = meta_data, structure_zeros = struc_zero)
return(res)
}
# ANCOM main function
ANCOM = function(feature_table, meta_data, struc_zero = NULL, main_var, p_adj_method = "BH",
alpha = 0.05, adj_formula = NULL, rand_formula = NULL, ...){
# OTU table transformation:
# (1) Discard taxa with structural zeros (if any); (2) Add pseudocount (1) and take logarithm.
if (!is.null(struc_zero)) {
num_struc_zero = apply(struc_zero, 1, sum)
comp_table = feature_table[num_struc_zero == 0, ]
}else{
comp_table = feature_table
}
comp_table = log(as.matrix(comp_table) + 1)
n_taxa = dim(comp_table)[1]
taxa_id = rownames(comp_table)
n_samp = dim(comp_table)[2]
# Determine the type of statistical test and its formula.
if (is.null(rand_formula) & is.null(adj_formula)) {
# Basic model
# Whether the main variable of interest has two levels or more?
if (length(unique(meta_data%>%pull(main_var))) == 2) {
# Two levels: Wilcoxon rank-sum test
tfun = exactRankTests::wilcox.exact
} else{
# More than two levels: Kruskal-Wallis test
tfun = stats::kruskal.test
}
# Formula
tformula = formula(paste("x ~", main_var, sep = " "))
}else if (is.null(rand_formula) & !is.null(adj_formula)) {
# Model: ANOVA
tfun = stats::aov
# Formula
tformula = formula(paste("x ~", main_var, "+", adj_formula, sep = " "))
}else if (!is.null(rand_formula)) {
# Model: Mixed-effects model
tfun = nlme::lme
# Formula
if (is.null(adj_formula)) {
# Random intercept model
tformula = formula(paste("x ~", main_var))
}else {
# Random coefficients/slope model
tformula = formula(paste("x ~", main_var, "+", adj_formula))
}
}
# Calculate the p-value for each pairwise comparison of taxa.
p_data = matrix(NA, nrow = n_taxa, ncol = n_taxa)
colnames(p_data) = taxa_id
rownames(p_data) = taxa_id
for (i in 1:(n_taxa - 1)) {
# Loop through each taxon.
# For each taxon i, additive log ratio (alr) transform the OTU table using taxon i as the reference.
# e.g. the first alr matrix will be the log abundance data (comp_table) recursively subtracted
# by the log abundance of 1st taxon (1st column) column-wisely, and remove the first i columns since:
# the first (i - 1) columns were calculated by previous iterations, and
# the i^th column contains all zeros.
alr_data = apply(comp_table, 1, function(x) x - comp_table[i, ])
# apply(...) allows crossing the data in a number of ways and avoid explicit use of loop constructs.
# Here, we basically want to iteratively subtract each column of the comp_table by its i^th column.
alr_data = alr_data[, - (1:i), drop = FALSE]
n_lr = dim(alr_data)[2] # number of log-ratios (lr)
alr_data = cbind(alr_data, meta_data) # merge with the metadata
# P-values
if (is.null(rand_formula) & is.null(adj_formula)) {
p_data[-(1:i), i] = apply(alr_data[, 1:n_lr, drop = FALSE], 2, function(x){
tfun(tformula, data = data.frame(x, alr_data, check.names = FALSE))$p.value
}
)
}else if (is.null(rand_formula) & !is.null(adj_formula)) {
p_data[-(1:i), i] = apply(alr_data[, 1:n_lr, drop = FALSE], 2, function(x){
fit = tfun(tformula,
data = data.frame(x, alr_data, check.names = FALSE),
na.action = na.omit)
summary(fit)[[1]][main_var, "Pr(>F)"]
}
)
}else if (!is.null(rand_formula)) {
p_data[-(1:i), i] = apply(alr_data[, 1:n_lr, drop = FALSE], 2, function(x){
fit = tfun(fixed = tformula,
data = data.frame(x, alr_data, check.names = FALSE),
random = formula(rand_formula),
na.action = na.omit, ...)
anova(fit)[main_var, "p-value"]
}
)
}
}
# Complete the p-value matrix.
# What we got from above iterations is a lower triangle matrix of p-values.
p_data[upper.tri(p_data)] = t(p_data)[upper.tri(p_data)]
diag(p_data) = 1 # let p-values on diagonal equal to 1
# Multiple comparisons correction.
q_data = apply(p_data, 2, function(x) p.adjust(x, method = p_adj_method))
# Calculate the W statistic of ANCOM.
# For each taxon, count the number of q-values < alpha.
W = apply(q_data, 2, function(x) sum(x < alpha))
# Organize outputs
out_comp = data.frame(taxa_id, W, row.names = NULL, check.names = FALSE)
# Declare a taxon to be differentially abundant based on the quantile of W statistic.
# We perform (n_taxa - 1) hypothesis testings on each taxon, so the maximum number of rejections is (n_taxa - 1).
out_comp = out_comp%>%mutate(detected_0.9 = ifelse(W > 0.9 * (n_taxa -1), TRUE, FALSE),
detected_0.8 = ifelse(W > 0.8 * (n_taxa -1), TRUE, FALSE),
detected_0.7 = ifelse(W > 0.7 * (n_taxa -1), TRUE, FALSE),
detected_0.6 = ifelse(W > 0.6 * (n_taxa -1), TRUE, FALSE))
# Taxa with structural zeros are automatically declared to be differentially abundant
if (!is.null(struc_zero)){
out = data.frame(taxa_id = rownames(struc_zero), W = Inf, detected_0.9 = TRUE,
detected_0.8 = TRUE, detected_0.7 = TRUE, detected_0.6 = TRUE,
row.names = NULL, check.names = FALSE)
out[match(taxa_id, out$taxa_id), ] = out_comp
}else{
out = out_comp
}
# Draw volcano plot
# Calculate clr
clr_table = apply(feature_table, 2, clr)
# Calculate clr mean difference
eff_size = apply(clr_table, 1, function(y)
lm(y ~ x, data = data.frame(y = y,
x = meta_data %>% pull(main_var),
check.names = FALSE))$coef[-1])
if (is.matrix(eff_size)){
# Data frame for the figure
dat_fig = data.frame(taxa_id = out$taxa_id, t(eff_size), y = out$W, check.names = FALSE) %>%
mutate(zero_ind = factor(ifelse(is.infinite(y), "Yes", "No"), levels = c("Yes", "No"))) %>%
gather(key = group, value = x, rownames(eff_size))
# Replcace "x" to the name of covariate
dat_fig$group = sapply(dat_fig$group, function(x) gsub("x", paste0(main_var, " = "), x))
# Replace Inf by (n_taxa - 1) for structural zeros
dat_fig$y = replace(dat_fig$y, is.infinite(dat_fig$y), n_taxa - 1)
fig = ggplot(data = dat_fig) + aes(x = x, y = y) +
geom_point(aes(color = zero_ind)) +
facet_wrap(~ group) +
labs(x = "CLR mean difference", y = "W statistic") +
scale_color_discrete(name = "Structural zero", drop = FALSE) +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5), legend.position = "top",
strip.background = element_rect(fill = "white"))
fig
} else{
# Data frame for the figure
dat_fig = data.frame(taxa_id = out$taxa_id, x = eff_size, y = out$W) %>%
mutate(zero_ind = factor(ifelse(is.infinite(y), "Yes", "No"), levels = c("Yes", "No")))
# Replace Inf by (n_taxa - 1) for structural zeros
dat_fig$y = replace(dat_fig$y, is.infinite(dat_fig$y), n_taxa - 1)
fig = ggplot(data = dat_fig) + aes(x = x, y = y) +
geom_point(aes(color = zero_ind)) +
labs(x = "CLR mean difference", y = "W statistic") +
scale_color_discrete(name = "Structural zero", drop = FALSE) +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5), legend.position = "top")
fig
}
res = list(out = out, fig = fig)
return(res)
}
ASV_table = ps %>%
filter_taxa(function(x) sum(x ) > 200 , TRUE) %>%
subset_samples(Group %in% id.g[,i]) %>%
vegan_otu() %>% t() %>%
as.data.frame()
groupings <- ps %>%
subset_samples(Group%in% id.g[,i]) %>%
sample_data()
groupings$ID = row.names(groupings)
groupings$Sample <- rownames(groupings)
prepro <- feature_table_pre_process(feature_table = ASV_table,
meta_data = groupings, sample_var = 'Sample',
group_var = NULL,
out_cut = 0.05,
zero_cut = 0.90,
lib_cut = 1000, neg_lb=FALSE)
feature_table <- prepro$feature_table
metadata <- prepro$meta_data
struc_zero <- prepro$structure_zerosMicrboial biomarker judgment
Code 5A（Example 28）
Top = 100
p.lvl = 0.05
lda.lvl = 1
seed = 11
adjust.p = F
ps = readRDS("./data/ps_liu.rds")
alltax = ps %>%
ggClusterNet::filter_OTU_ps(100) %>%
ggClusterNet::vegan_tax() %>%
as.data.frame()
alltax$OTU = row.names(alltax)
alltax$Kingdom = paste(alltax$Kingdom,sep = "_Rank_")
alltax$Phylum = paste(alltax$Kingdom,alltax$Phylum,sep = "_Rank_")
alltax$Class = paste(alltax$Phylum,alltax$Class,sep = "_Rank_")
alltax$Order = paste(alltax$Class,alltax$Order,sep = "_Rank_")
alltax$Family = paste(alltax$Order,alltax$Family,sep = "_Rank_")
alltax$Genus = paste(alltax$Family,alltax$Genus,sep = "_Rank_")
alltax$Species = paste(alltax$Genus,alltax$Species,sep = "_Rank_")
alltax[is.na(alltax)] = "Unknown"
trda <- MicrobiotaProcess::convert_to_treedata(alltax)
p <- ggtree(trda, layout="circular", size=0.2, xlim=c(30,NA)) +
geom_point(
pch = 21,
size=3,
alpha=1,
fill = "#FFFFB3")
p$data$lab2 <- p$data$label %>% strsplit( "_Rank_") %>%
sapply(function(x) x[length(x)])
p$data$lab2 = gsub("st__","",p$data$lab2 )
p$data$nodeSize = 1
otu = ps %>%
ggClusterNet::filter_OTU_ps(100) %>%
ggClusterNet::vegan_otu() %>%
t() %>%
as.data.frame()
otu_tax = merge(otu,alltax,by = "row.names",all = F)
head(otu_tax)
rank1 <- otu_tax %>%
dplyr::group_by(Kingdom) %>%
dplyr::summarise_if(is.numeric, sum, na.rm = TRUE)
colnames(rank1)[1] = "id"
rank1$id = paste("k__",rank1$id,sep = "")
rank2 <- otu_tax %>%
dplyr::group_by(Phylum) %>%
dplyr::summarise_if(is.numeric, sum, na.rm = TRUE)
colnames(rank2)[1] = "id"
rank2$id = paste("p__",rank2$id,sep = "")
rank3 <- otu_tax %>%
dplyr::group_by(Class) %>%
dplyr::summarise_if(is.numeric, sum, na.rm = TRUE)
colnames(rank3)[1] = "id"
rank3$id = paste("c__",rank3$id,sep = "")
rank4 <- otu_tax %>%
dplyr::group_by(Order) %>%
dplyr::summarise_if(is.numeric, sum, na.rm = TRUE)
colnames(rank4)[1] = "id"
rank4$id = paste("o__",rank4$id,sep = "")
rank5 <- otu_tax %>%
dplyr::group_by(Family) %>%
dplyr::summarise_if(is.numeric, sum, na.rm = TRUE)
colnames(rank5)[1] = "id"
rank5$id = paste("f__",rank5$id,sep = "")
rank6 <- otu_tax %>%
dplyr::group_by(Genus) %>%
dplyr::summarise_if(is.numeric, sum, na.rm = TRUE)
colnames(rank6)[1] = "id"
rank6$id = paste("g__",rank6$id,sep = "")
rank7 <- otu_tax %>%
dplyr::group_by(Species) %>%
dplyr::summarise_if(is.numeric, sum, na.rm = TRUE)
colnames(rank7)[1] = "id"
rank7$id = paste("s__",rank7$id,sep = "")
rank8 <- otu_tax %>%
dplyr::group_by(OTU) %>%
dplyr::summarise_if(is.numeric, sum, na.rm = TRUE)
colnames(rank8)[1] = "id"
rank8$id = paste("st__",rank8$id,sep = "")
all = rbind(rank1,rank2,rank3,rank4,rank5,rank6,rank7,rank8)
data1 = as.data.frame(all)
row.names(data1) = data1$id
data1$id = NULL
ps_G_graphlan = phyloseq::phyloseq(phyloseq::otu_table(as.matrix(data1),taxa_are_rows = TRUE),
phyloseq::sample_data(ps))
ps_G_graphlan
#> phyloseq-class experiment-level object
#> otu_table() OTU Table: [ 284 taxa and 18 samples ]
#> sample_data() Sample Data: [ 18 samples by 10 sample variables ]
otu = as.data.frame((ggClusterNet::vegan_otu(ps_G_graphlan)))
otu[otu==0] <- 1
map = as.data.frame(phyloseq::sample_data(ps_G_graphlan))
# otu = (otu_table)
claslbl= map$Group %>% as.factor()
set.seed(100)
rawpvalues <- apply(otu, 2, function(x) kruskal.test(x, claslbl)$p.value);
ord.inx <- order(rawpvalues)
rawpvalues <- rawpvalues[ord.inx]
clapvalues <- p.adjust(rawpvalues, method ="fdr")
# p.adjust
wil_datadf <- as.data.frame(otu[,ord.inx])
ldares <- MASS::lda(claslbl~ .,data = wil_datadf)
# ldares
ldamean <- as.data.frame(t(ldares$means))
ldameanclass_no <<- length(unique(claslbl))
ldamean$max <- apply(ldamean[,1:class_no],1,max);
ldamean$min <- apply(ldamean[,1:class_no],1,min);
ldamean$LDAscore <- signif(log10(1+abs(ldamean$max-ldamean$min)/2),digits=3);
head(ldamean)
a = rep("A",length(ldamean$max))
for (i in 1:length(ldamean$max)) {
name =colnames(ldamean[,1:class_no])
a[i] = name[ldamean[,1:class_no][i,] %in% ldamean$max[i]]}
ldamean$class = a
tem1 = row.names(ldamean)
tem1 %>% as.character()
#> [1] "st__ASV_6"
#> [2] "st__ASV_60"
#> [3] "f__Bacteria_Rank_Proteobacteria_Rank_Betaproteobacteria_Rank_Burkholderiales_Rank_Unassigned"
#> [4] "g__Bacteria_Rank_Proteobacteria_Rank_Betaproteobacteria_Rank_Burkholderiales_Rank_Unassigned_Rank_Unassigned"
#> [5] "s__Bacteria_Rank_Proteobacteria_Rank_Betaproteobacteria_Rank_Burkholderiales_Rank_Unassigned_Rank_Unassigned_Rank_Unassigned"
#> [6] "st__ASV_17"
#> [7] "st__ASV_41"
#> [8] "st__ASV_73"
#> [9] "st__ASV_39"
#> [10] "o__Bacteria_Rank_Proteobacteria_Rank_Alphaproteobacteria_Rank_Caulobacterales"
#> [11] "f__Bacteria_Rank_Proteobacteria_Rank_Alphaproteobacteria_Rank_Caulobacterales_Rank_Caulobacteraceae"
#> [12] "g__Bacteria_Rank_Proteobacteria_Rank_Alphaproteobacteria_Rank_Caulobacterales_Rank_Caulobacteraceae_Rank_Phenylobacterium"
#> [13] "s__Bacteria_Rank_Proteobacteria_Rank_Alphaproteobacteria_Rank_Caulobacterales_Rank_Caulobacteraceae_Rank_Phenylobacterium_Rank_Phenylobacterium_composti"
#> [14] "st__ASV_63"
#> [15] "f__Bacteria_Rank_Proteobacteria_Rank_Alphaproteobacteria_Rank_Rhizobiales_Rank_Hyphomicrobiaceae"
#> [16] "g__Bacteria_Rank_Proteobacteria_Rank_Alphaproteobacteria_Rank_Rhizobiales_Rank_Hyphomicrobiaceae_Rank_Devosia"
#> [17] "s__Bacteria_Rank_Proteobacteria_Rank_Alphaproteobacteria_Rank_Rhizobiales_Rank_Hyphomicrobiaceae_Rank_Devosia_Rank_Unassigned"
#> [18] "st__ASV_56"
#> [19] "st__ASV_79"
#> [20] "g__Bacteria_Rank_Proteobacteria_Rank_Gammaproteobacteria_Rank_Xanthomonadales_Rank_Sinobacteraceae_Rank_Steroidobacter"
#> [21] "s__Bacteria_Rank_Proteobacteria_Rank_Gammaproteobacteria_Rank_Xanthomonadales_Rank_Sinobacteraceae_Rank_Steroidobacter_Rank_Unassigned"
#> [22] "f__Bacteria_Rank_Proteobacteria_Rank_Gammaproteobacteria_Rank_Xanthomonadales_Rank_Sinobacteraceae"
#> [23] "p__Bacteria_Rank_Firmicutes"
#> [24] "c__Bacteria_Rank_Firmicutes_Rank_Bacilli"
#> [25] "o__Bacteria_Rank_Firmicutes_Rank_Bacilli_Rank_Bacillales"
#> [26] "f__Bacteria_Rank_Firmicutes_Rank_Bacilli_Rank_Bacillales_Rank_Bacillaceae_1"
#> [27] "g__Bacteria_Rank_Firmicutes_Rank_Bacilli_Rank_Bacillales_Rank_Bacillaceae_1_Rank_Bacillus"
#> [28] "s__Bacteria_Rank_Firmicutes_Rank_Bacilli_Rank_Bacillales_Rank_Bacillaceae_1_Rank_Bacillus_Rank_Unassigned"
#> [29] "st__ASV_43"
#> [30] "st__ASV_37"
#> [31] "c__Bacteria_Rank_Proteobacteria_Rank_Betaproteobacteria"
#> [32] "o__Bacteria_Rank_Proteobacteria_Rank_Betaproteobacteria_Rank_Burkholderiales"
#> [33] "o__Bacteria_Rank_Proteobacteria_Rank_Deltaproteobacteria_Rank_Myxococcales"
#> [34] "f__Bacteria_Rank_Proteobacteria_Rank_Deltaproteobacteria_Rank_Myxococcales_Rank_Sandaracinaceae"
#> [35] "g__Bacteria_Rank_Proteobacteria_Rank_Deltaproteobacteria_Rank_Myxococcales_Rank_Sandaracinaceae_Rank_Sandaracinus"
#> [36] "s__Bacteria_Rank_Proteobacteria_Rank_Deltaproteobacteria_Rank_Myxococcales_Rank_Sandaracinaceae_Rank_Sandaracinus_Rank_Sandaracinus_amylolyticus"
#> [37] "st__ASV_114"
#> [38] "g__Bacteria_Rank_Proteobacteria_Rank_Betaproteobacteria_Rank_Burkholderiales_Rank_Comamonadaceae_Rank_Pelomonas"
#> [39] "s__Bacteria_Rank_Proteobacteria_Rank_Betaproteobacteria_Rank_Burkholderiales_Rank_Comamonadaceae_Rank_Pelomonas_Rank_Pelomonas_puraquae"
#> [40] "st__ASV_2"
#> [41] "g__Bacteria_Rank_Proteobacteria_Rank_Betaproteobacteria_Rank_Burkholderiales_Rank_Burkholderiales_incertae_sedis_Rank_Piscinibacter"
#> [42] "s__Bacteria_Rank_Proteobacteria_Rank_Betaproteobacteria_Rank_Burkholderiales_Rank_Burkholderiales_incertae_sedis_Rank_Piscinibacter_Rank_Piscinibacter_aquaticus"
#> [43] "st__ASV_55"
#> [44] "st__ASV_47"
#> [45] "c__Bacteria_Rank_Proteobacteria_Rank_Unassigned"
#> [46] "o__Bacteria_Rank_Proteobacteria_Rank_Unassigned_Rank_Unassigned"
#> [47] "f__Bacteria_Rank_Proteobacteria_Rank_Unassigned_Rank_Unassigned_Rank_Unassigned"
#> [48] "g__Bacteria_Rank_Proteobacteria_Rank_Betaproteobacteria_Rank_Burkholderiales_Rank_Oxalobacteraceae_Rank_Unassigned"
#> [49] "g__Bacteria_Rank_Proteobacteria_Rank_Unassigned_Rank_Unassigned_Rank_Unassigned_Rank_Unassigned"
#> [50] "s__Bacteria_Rank_Proteobacteria_Rank_Betaproteobacteria_Rank_Burkholderiales_Rank_Oxalobacteraceae_Rank_Unassigned_Rank_Unassigned"
#> [51] "s__Bacteria_Rank_Proteobacteria_Rank_Unassigned_Rank_Unassigned_Rank_Unassigned_Rank_Unassigned_Rank_Unassigned"
#> [52] "s__Bacteria_Rank_Proteobacteria_Rank_Gammaproteobacteria_Rank_Pseudomonadales_Rank_Pseudomonadaceae_Rank_Pseudomonas_Rank_Pseudomonas_chlororaphis_subsp._piscium"
#> [53] "st__ASV_67"
#> [54] "k__Bacteria"
#> [55] "st__ASV_50"
#> [56] "p__Bacteria_Rank_Proteobacteria"
#> [57] "f__Bacteria_Rank_Proteobacteria_Rank_Betaproteobacteria_Rank_Burkholderiales_Rank_Comamonadaceae"
#> [58] "g__Bacteria_Rank_Proteobacteria_Rank_Alphaproteobacteria_Rank_Sphingomonadales_Rank_Sphingomonadaceae_Rank_Sphingomonas"
#> [59] "s__Bacteria_Rank_Proteobacteria_Rank_Alphaproteobacteria_Rank_Sphingomonadales_Rank_Sphingomonadaceae_Rank_Sphingomonas_Rank_Unassigned"
#> [60] "st__ASV_24"
#> [61] "g__Bacteria_Rank_Proteobacteria_Rank_Betaproteobacteria_Rank_Burkholderiales_Rank_Comamonadaceae_Rank_Polaromonas"
#> [62] "s__Bacteria_Rank_Proteobacteria_Rank_Betaproteobacteria_Rank_Burkholderiales_Rank_Comamonadaceae_Rank_Polaromonas_Rank_Unassigned"
#> [63] "st__ASV_104"
#> [64] "st__ASV_44"
#> [65] "st__ASV_33"
#> [66] "c__Bacteria_Rank_Proteobacteria_Rank_Gammaproteobacteria"
#> [67] "o__Bacteria_Rank_Proteobacteria_Rank_Gammaproteobacteria_Rank_Xanthomonadales"
#> [68] "o__Bacteria_Rank_Proteobacteria_Rank_Alphaproteobacteria_Rank_Sphingomonadales"
#> [69] "f__Bacteria_Rank_Proteobacteria_Rank_Alphaproteobacteria_Rank_Sphingomonadales_Rank_Sphingomonadaceae"
#> [70] "g__Bacteria_Rank_Proteobacteria_Rank_Alphaproteobacteria_Rank_Rhizobiales_Rank_Rhizobiaceae_Rank_Rhizobium"
#> [71] "s__Bacteria_Rank_Proteobacteria_Rank_Alphaproteobacteria_Rank_Rhizobiales_Rank_Rhizobiaceae_Rank_Rhizobium_Rank_Unassigned"
#> [72] "st__ASV_10"
#> [73] "s__Bacteria_Rank_Proteobacteria_Rank_Alphaproteobacteria_Rank_Rhizobiales_Rank_Hyphomicrobiaceae_Rank_Devosia_Rank_Devosia_insulae"
#> [74] "st__ASV_96"
#> [75] "st__ASV_14"
#> [76] "g__Bacteria_Rank_Actinobacteria_Rank_Actinobacteria_Rank_Actinomycetales_Rank_Nocardioidaceae_Rank_Kribbella"
#> [77] "s__Bacteria_Rank_Actinobacteria_Rank_Actinobacteria_Rank_Actinomycetales_Rank_Nocardioidaceae_Rank_Kribbella_Rank_Kribbella_catacumbae"
#> [78] "st__ASV_21"
#> [79] "f__Bacteria_Rank_Actinobacteria_Rank_Actinobacteria_Rank_Actinomycetales_Rank_Kineosporiaceae"
#> [80] "g__Bacteria_Rank_Actinobacteria_Rank_Actinobacteria_Rank_Actinomycetales_Rank_Kineosporiaceae_Rank_Kineosporia"
#> [81] "s__Bacteria_Rank_Actinobacteria_Rank_Actinobacteria_Rank_Actinomycetales_Rank_Kineosporiaceae_Rank_Kineosporia_Rank_Kineosporia_rhamnosa"
#> [82] "st__ASV_34"
#> [83] "g__Bacteria_Rank_Actinobacteria_Rank_Actinobacteria_Rank_Actinomycetales_Rank_Pseudonocardiaceae_Rank_Amycolatopsis"
#> [84] "c__Bacteria_Rank_Proteobacteria_Rank_Alphaproteobacteria"
#> [85] "g__Bacteria_Rank_Actinobacteria_Rank_Actinobacteria_Rank_Actinomycetales_Rank_Thermomonosporaceae_Rank_Unassigned"
#> [86] "s__Bacteria_Rank_Actinobacteria_Rank_Actinobacteria_Rank_Actinomycetales_Rank_Thermomonosporaceae_Rank_Unassigned_Rank_Unassigned"
#> [87] "st__ASV_1"
#> [88] "st__ASV_35"
#> [89] "st__ASV_166"
#> [90] "s__Bacteria_Rank_Proteobacteria_Rank_Gammaproteobacteria_Rank_Xanthomonadales_Rank_Sinobacteraceae_Rank_Steroidobacter_Rank_Steroidobacter_denitrificans"
#> [91] "st__ASV_77"
#> [92] "g__Bacteria_Rank_Actinobacteria_Rank_Actinobacteria_Rank_Actinomycetales_Rank_Micromonosporaceae_Rank_Couchioplanes"
#> [93] "s__Bacteria_Rank_Actinobacteria_Rank_Actinobacteria_Rank_Actinomycetales_Rank_Micromonosporaceae_Rank_Couchioplanes_Rank_Unassigned"
#> [94] "st__ASV_86"
#> [95] "s__Bacteria_Rank_Proteobacteria_Rank_Gammaproteobacteria_Rank_Xanthomonadales_Rank_Sinobacteraceae_Rank_Steroidobacter_Rank_Steroidobacter_agariperforans"
#> [96] "st__ASV_89"
#> [97] "f__Bacteria_Rank_Actinobacteria_Rank_Actinobacteria_Rank_Actinomycetales_Rank_Thermomonosporaceae"
#> [98] "st__ASV_91"
#> [99] "g__Bacteria_Rank_Proteobacteria_Rank_Betaproteobacteria_Rank_Burkholderiales_Rank_Comamonadaceae_Rank_Kinneretia"
#> [100] "s__Bacteria_Rank_Proteobacteria_Rank_Betaproteobacteria_Rank_Burkholderiales_Rank_Comamonadaceae_Rank_Kinneretia_Rank_Kinneretia_asaccharophila"
#> [101] "o__Bacteria_Rank_Proteobacteria_Rank_Alphaproteobacteria_Rank_Rhizobiales"
#> [102] "f__Bacteria_Rank_Actinobacteria_Rank_Actinobacteria_Rank_Actinomycetales_Rank_Unassigned"
#> [103] "g__Bacteria_Rank_Actinobacteria_Rank_Actinobacteria_Rank_Actinomycetales_Rank_Unassigned_Rank_Unassigned"
#> [104] "s__Bacteria_Rank_Actinobacteria_Rank_Actinobacteria_Rank_Actinomycetales_Rank_Unassigned_Rank_Unassigned_Rank_Unassigned"
#> [105] "st__ASV_95"
#> [106] "o__Bacteria_Rank_Proteobacteria_Rank_Alphaproteobacteria_Rank_Rhodospirillales"
#> [107] "f__Bacteria_Rank_Proteobacteria_Rank_Alphaproteobacteria_Rank_Rhodospirillales_Rank_Rhodospirillaceae"
#> [108] "g__Bacteria_Rank_Proteobacteria_Rank_Alphaproteobacteria_Rank_Rhodospirillales_Rank_Rhodospirillaceae_Rank_Dongia"
#> [109] "s__Bacteria_Rank_Proteobacteria_Rank_Alphaproteobacteria_Rank_Rhodospirillales_Rank_Rhodospirillaceae_Rank_Dongia_Rank_Dongia_mobilis"
#> [110] "st__ASV_66"
#> [111] "st__ASV_64"
#> [112] "g__Bacteria_Rank_Proteobacteria_Rank_Gammaproteobacteria_Rank_Pseudomonadales_Rank_Pseudomonadaceae_Rank_Rhizobacter"
#> [113] "g__Bacteria_Rank_Proteobacteria_Rank_Gammaproteobacteria_Rank_Pseudomonadales_Rank_Pseudomonadaceae_Rank_Pseudomonas"
#> [114] "p__Bacteria_Rank_Actinobacteria"
#> [115] "c__Bacteria_Rank_Actinobacteria_Rank_Actinobacteria"
#> [116] "o__Bacteria_Rank_Actinobacteria_Rank_Actinobacteria_Rank_Actinomycetales"
#> [117] "s__Bacteria_Rank_Proteobacteria_Rank_Alphaproteobacteria_Rank_Rhizobiales_Rank_Rhizobiaceae_Rank_Ensifer_Rank_Ensifer_americanus"
#> [118] "st__ASV_62"
#> [119] "g__Bacteria_Rank_Actinobacteria_Rank_Actinobacteria_Rank_Actinomycetales_Rank_Pseudonocardiaceae_Rank_Lentzea"
#> [120] "st__ASV_54"
#> [121] "f__Bacteria_Rank_Proteobacteria_Rank_Alphaproteobacteria_Rank_Rhizobiales_Rank_Rhizobiaceae"
#> [122] "f__Bacteria_Rank_Proteobacteria_Rank_Betaproteobacteria_Rank_Burkholderiales_Rank_Oxalobacteraceae"
#> [123] "c__Bacteria_Rank_Proteobacteria_Rank_Deltaproteobacteria"
#> [124] "g__Bacteria_Rank_Proteobacteria_Rank_Alphaproteobacteria_Rank_Rhizobiales_Rank_Rhizobiaceae_Rank_Unassigned"
#> [125] "s__Bacteria_Rank_Proteobacteria_Rank_Alphaproteobacteria_Rank_Rhizobiales_Rank_Rhizobiaceae_Rank_Unassigned_Rank_Unassigned"
#> [126] "st__ASV_29"
#> [127] "f__Bacteria_Rank_Actinobacteria_Rank_Actinobacteria_Rank_Actinomycetales_Rank_Pseudonocardiaceae"
#> [128] "s__Bacteria_Rank_Actinobacteria_Rank_Actinobacteria_Rank_Actinomycetales_Rank_Pseudonocardiaceae_Rank_Amycolatopsis_Rank_Amycolatopsis_keratiniphila_subsp._keratiniphila"
#> [129] "st__ASV_23"
#> [130] "st__ASV_18"
#> [131] "st__ASV_85"
#> [132] "g__Bacteria_Rank_Proteobacteria_Rank_Gammaproteobacteria_Rank_Xanthomonadales_Rank_Sinobacteraceae_Rank_Unassigned"
#> [133] "s__Bacteria_Rank_Proteobacteria_Rank_Gammaproteobacteria_Rank_Xanthomonadales_Rank_Sinobacteraceae_Rank_Unassigned_Rank_Unassigned"
#> [134] "st__ASV_46"
#> [135] "g__Bacteria_Rank_Proteobacteria_Rank_Betaproteobacteria_Rank_Burkholderiales_Rank_Comamonadaceae_Rank_Unassigned"
#> [136] "s__Bacteria_Rank_Proteobacteria_Rank_Betaproteobacteria_Rank_Burkholderiales_Rank_Comamonadaceae_Rank_Unassigned_Rank_Unassigned"
#> [137] "st__ASV_83"
#> [138] "f__Bacteria_Rank_Actinobacteria_Rank_Actinobacteria_Rank_Actinomycetales_Rank_Streptomycetaceae"
#> [139] "g__Bacteria_Rank_Actinobacteria_Rank_Actinobacteria_Rank_Actinomycetales_Rank_Streptomycetaceae_Rank_Streptomyces"
#> [140] "o__Bacteria_Rank_Proteobacteria_Rank_Gammaproteobacteria_Rank_Pseudomonadales"
#> [141] "f__Bacteria_Rank_Proteobacteria_Rank_Gammaproteobacteria_Rank_Pseudomonadales_Rank_Pseudomonadaceae"
#> [142] "st__ASV_32"
#> [143] "s__Bacteria_Rank_Actinobacteria_Rank_Actinobacteria_Rank_Actinomycetales_Rank_Streptomycetaceae_Rank_Streptomyces_Rank_Unassigned"
#> [144] "st__ASV_8"
#> [145] "st__ASV_22"
#> [146] "s__Bacteria_Rank_Actinobacteria_Rank_Actinobacteria_Rank_Actinomycetales_Rank_Pseudonocardiaceae_Rank_Amycolatopsis_Rank_Unassigned"
#> [147] "st__ASV_20"
#> [148] "s__Bacteria_Rank_Actinobacteria_Rank_Actinobacteria_Rank_Actinomycetales_Rank_Streptomycetaceae_Rank_Streptomyces_Rank_Streptomyces_ederensis"
#> [149] "st__ASV_16"
#> [150] "g__Bacteria_Rank_Actinobacteria_Rank_Actinobacteria_Rank_Actinomycetales_Rank_Thermomonosporaceae_Rank_Actinocorallia"
#> [151] "s__Bacteria_Rank_Actinobacteria_Rank_Actinobacteria_Rank_Actinomycetales_Rank_Thermomonosporaceae_Rank_Actinocorallia_Rank_Unassigned"
#> [152] "st__ASV_28"
#> [153] "s__Bacteria_Rank_Proteobacteria_Rank_Gammaproteobacteria_Rank_Pseudomonadales_Rank_Pseudomonadaceae_Rank_Rhizobacter_Rank_Rhizobacter_bergeniae"
#> [154] "st__ASV_3"
#> [155] "st__ASV_74"
#> [156] "st__ASV_15"
#> [157] "f__Bacteria_Rank_Actinobacteria_Rank_Actinobacteria_Rank_Actinomycetales_Rank_Nocardioidaceae"
#> [158] "st__ASV_40"
#> [159] "f__Bacteria_Rank_Proteobacteria_Rank_Betaproteobacteria_Rank_Burkholderiales_Rank_Burkholderiales_incertae_sedis"
#> [160] "s__Bacteria_Rank_Actinobacteria_Rank_Actinobacteria_Rank_Actinomycetales_Rank_Pseudonocardiaceae_Rank_Lentzea_Rank_Lentzea_kentuckyensis"
#> [161] "st__ASV_45"
#> [162] "g__Bacteria_Rank_Proteobacteria_Rank_Betaproteobacteria_Rank_Burkholderiales_Rank_Oxalobacteraceae_Rank_Pseudoduganella"
#> [163] "s__Bacteria_Rank_Proteobacteria_Rank_Betaproteobacteria_Rank_Burkholderiales_Rank_Oxalobacteraceae_Rank_Pseudoduganella_Rank_Pseudoduganella_violaceinigra"
#> [164] "o__Bacteria_Rank_Proteobacteria_Rank_Deltaproteobacteria_Rank_Unassigned"
#> [165] "f__Bacteria_Rank_Proteobacteria_Rank_Deltaproteobacteria_Rank_Unassigned_Rank_Unassigned"
#> [166] "g__Bacteria_Rank_Proteobacteria_Rank_Deltaproteobacteria_Rank_Unassigned_Rank_Unassigned_Rank_Unassigned"
#> [167] "s__Bacteria_Rank_Proteobacteria_Rank_Alphaproteobacteria_Rank_Rhizobiales_Rank_Rhizobiaceae_Rank_Ensifer_Rank_Unassigned"
#> [168] "s__Bacteria_Rank_Proteobacteria_Rank_Deltaproteobacteria_Rank_Unassigned_Rank_Unassigned_Rank_Unassigned_Rank_Unassigned"
#> [169] "st__ASV_48"
#> [170] "st__ASV_87"
#> [171] "f__Bacteria_Rank_Proteobacteria_Rank_Alphaproteobacteria_Rank_Rhizobiales_Rank_Unassigned"
#> [172] "g__Bacteria_Rank_Proteobacteria_Rank_Alphaproteobacteria_Rank_Rhizobiales_Rank_Unassigned_Rank_Unassigned"
#> [173] "s__Bacteria_Rank_Proteobacteria_Rank_Alphaproteobacteria_Rank_Rhizobiales_Rank_Unassigned_Rank_Unassigned_Rank_Unassigned"
#> [174] "g__Bacteria_Rank_Proteobacteria_Rank_Betaproteobacteria_Rank_Burkholderiales_Rank_Oxalobacteraceae_Rank_Noviherbaspirillum"
#> [175] "s__Bacteria_Rank_Proteobacteria_Rank_Betaproteobacteria_Rank_Burkholderiales_Rank_Oxalobacteraceae_Rank_Noviherbaspirillum_Rank_Noviherbaspirillum_suwonense"
#> [176] "st__ASV_61"
#> [177] "s__Bacteria_Rank_Actinobacteria_Rank_Actinobacteria_Rank_Actinomycetales_Rank_Pseudonocardiaceae_Rank_Lentzea_Rank_Unassigned"
#> [178] "st__ASV_42"
#> [179] "s__Bacteria_Rank_Proteobacteria_Rank_Gammaproteobacteria_Rank_Pseudomonadales_Rank_Pseudomonadaceae_Rank_Rhizobacter_Rank_Unassigned"
#> [180] "st__ASV_4"
#> [181] "g__Bacteria_Rank_Actinobacteria_Rank_Actinobacteria_Rank_Actinomycetales_Rank_Nocardioidaceae_Rank_Nocardioides"
#> [182] "s__Bacteria_Rank_Actinobacteria_Rank_Actinobacteria_Rank_Actinomycetales_Rank_Nocardioidaceae_Rank_Nocardioides_Rank_Unassigned"
#> [183] "g__Bacteria_Rank_Proteobacteria_Rank_Gammaproteobacteria_Rank_Xanthomonadales_Rank_Xanthomonadaceae_Rank_Lysobacter"
#> [184] "g__Bacteria_Rank_Actinobacteria_Rank_Actinobacteria_Rank_Actinomycetales_Rank_Micromonosporaceae_Rank_Unassigned"
#> [185] "s__Bacteria_Rank_Actinobacteria_Rank_Actinobacteria_Rank_Actinomycetales_Rank_Micromonosporaceae_Rank_Unassigned_Rank_Unassigned"
#> [186] "st__ASV_52"
#> [187] "g__Bacteria_Rank_Proteobacteria_Rank_Betaproteobacteria_Rank_Burkholderiales_Rank_Burkholderiales_incertae_sedis_Rank_Aquabacterium"
#> [188] "s__Bacteria_Rank_Proteobacteria_Rank_Betaproteobacteria_Rank_Burkholderiales_Rank_Burkholderiales_incertae_sedis_Rank_Aquabacterium_Rank_Unassigned"
#> [189] "st__ASV_94"
#> [190] "g__Bacteria_Rank_Proteobacteria_Rank_Betaproteobacteria_Rank_Burkholderiales_Rank_Comamonadaceae_Rank_Variovorax"
#> [191] "s__Bacteria_Rank_Proteobacteria_Rank_Betaproteobacteria_Rank_Burkholderiales_Rank_Comamonadaceae_Rank_Variovorax_Rank_Unassigned"
#> [192] "st__ASV_81"
#> [193] "st__ASV_72"
#> [194] "st__ASV_11"
#> [195] "st__ASV_36"
#> [196] "st__ASV_59"
#> [197] "p__Bacteria_Rank_Unassigned"
#> [198] "c__Bacteria_Rank_Unassigned_Rank_Unassigned"
#> [199] "o__Bacteria_Rank_Unassigned_Rank_Unassigned_Rank_Unassigned"
#> [200] "f__Bacteria_Rank_Unassigned_Rank_Unassigned_Rank_Unassigned_Rank_Unassigned"
#> [201] "g__Bacteria_Rank_Unassigned_Rank_Unassigned_Rank_Unassigned_Rank_Unassigned_Rank_Unassigned"
#> [202] "s__Bacteria_Rank_Unassigned_Rank_Unassigned_Rank_Unassigned_Rank_Unassigned_Rank_Unassigned_Rank_Unassigned"
#> [203] "st__ASV_88"
#> [204] "s__Bacteria_Rank_Actinobacteria_Rank_Actinobacteria_Rank_Actinomycetales_Rank_Pseudonocardiaceae_Rank_Lentzea_Rank_Lentzea_flaviverrucosa"
#> [205] "s__Bacteria_Rank_Actinobacteria_Rank_Actinobacteria_Rank_Actinomycetales_Rank_Pseudonocardiaceae_Rank_Lentzea_Rank_Lentzea_albida"
#> [206] "st__ASV_49"
#> [207] "f__Bacteria_Rank_Proteobacteria_Rank_Alphaproteobacteria_Rank_Rhizobiales_Rank_Bradyrhizobiaceae"
#> [208] "st__ASV_25"
#> [209] "st__ASV_101"
#> [210] "g__Bacteria_Rank_Proteobacteria_Rank_Alphaproteobacteria_Rank_Rhizobiales_Rank_Bradyrhizobiaceae_Rank_Unassigned"
#> [211] "s__Bacteria_Rank_Proteobacteria_Rank_Alphaproteobacteria_Rank_Rhizobiales_Rank_Bradyrhizobiaceae_Rank_Unassigned_Rank_Unassigned"
#> [212] "st__ASV_65"
#> [213] "o__Bacteria_Rank_Proteobacteria_Rank_Gammaproteobacteria_Rank_Unassigned"
#> [214] "f__Bacteria_Rank_Proteobacteria_Rank_Gammaproteobacteria_Rank_Unassigned_Rank_Unassigned"
#> [215] "g__Bacteria_Rank_Proteobacteria_Rank_Gammaproteobacteria_Rank_Unassigned_Rank_Unassigned_Rank_Unassigned"
#> [216] "s__Bacteria_Rank_Proteobacteria_Rank_Gammaproteobacteria_Rank_Unassigned_Rank_Unassigned_Rank_Unassigned_Rank_Unassigned"
#> [217] "st__ASV_102"
#> [218] "s__Bacteria_Rank_Bacteroidetes_Rank_Flavobacteriia_Rank_Flavobacteriales_Rank_Flavobacteriaceae_Rank_Flavobacterium_Rank_Flavobacterium_terrae"
#> [219] "st__ASV_12"
#> [220] "st__ASV_57"
#> [221] "f__Bacteria_Rank_Actinobacteria_Rank_Actinobacteria_Rank_Actinomycetales_Rank_Micromonosporaceae"
#> [222] "g__Bacteria_Rank_Proteobacteria_Rank_Alphaproteobacteria_Rank_Rhizobiales_Rank_Bradyrhizobiaceae_Rank_Bradyrhizobium"
#> [223] "s__Bacteria_Rank_Proteobacteria_Rank_Alphaproteobacteria_Rank_Rhizobiales_Rank_Bradyrhizobiaceae_Rank_Bradyrhizobium_Rank_Bradyrhizobium_neotropicale"
#> [224] "st__ASV_75"
#> [225] "p__Bacteria_Rank_Chloroflexi"
#> [226] "c__Bacteria_Rank_Chloroflexi_Rank_Chloroflexia"
#> [227] "o__Bacteria_Rank_Chloroflexi_Rank_Chloroflexia_Rank_Herpetosiphonales"
#> [228] "f__Bacteria_Rank_Chloroflexi_Rank_Chloroflexia_Rank_Herpetosiphonales_Rank_Herpetosiphonaceae"
#> [229] "g__Bacteria_Rank_Chloroflexi_Rank_Chloroflexia_Rank_Herpetosiphonales_Rank_Herpetosiphonaceae_Rank_Herpetosiphon"
#> [230] "s__Bacteria_Rank_Chloroflexi_Rank_Chloroflexia_Rank_Herpetosiphonales_Rank_Herpetosiphonaceae_Rank_Herpetosiphon_Rank_Unassigned"
#> [231] "s__Bacteria_Rank_Proteobacteria_Rank_Gammaproteobacteria_Rank_Xanthomonadales_Rank_Xanthomonadaceae_Rank_Pseudoxanthomonas_Rank_Pseudoxanthomonas_gei"
#> [232] "st__ASV_70"
#> [233] "f__Bacteria_Rank_Proteobacteria_Rank_Gammaproteobacteria_Rank_Xanthomonadales_Rank_Xanthomonadaceae"
#> [234] "s__Bacteria_Rank_Proteobacteria_Rank_Gammaproteobacteria_Rank_Xanthomonadales_Rank_Xanthomonadaceae_Rank_Lysobacter_Rank_Unassigned"
#> [235] "st__ASV_76"
#> [236] "s__Bacteria_Rank_Proteobacteria_Rank_Gammaproteobacteria_Rank_Xanthomonadales_Rank_Xanthomonadaceae_Rank_Lysobacter_Rank_Lysobacter_brunescens"
#> [237] "st__ASV_93"
#> [238] "g__Bacteria_Rank_Proteobacteria_Rank_Alphaproteobacteria_Rank_Rhizobiales_Rank_Rhizobiaceae_Rank_Ensifer"
#> [239] "st__ASV_78"
#> [240] "st__ASV_51"
#> [241] "st__ASV_38"
#> [242] "g__Bacteria_Rank_Proteobacteria_Rank_Betaproteobacteria_Rank_Burkholderiales_Rank_Burkholderiales_incertae_sedis_Rank_Unassigned"
#> [243] "s__Bacteria_Rank_Proteobacteria_Rank_Betaproteobacteria_Rank_Burkholderiales_Rank_Burkholderiales_incertae_sedis_Rank_Unassigned_Rank_Unassigned"
#> [244] "st__ASV_30"
#> [245] "g__Bacteria_Rank_Proteobacteria_Rank_Gammaproteobacteria_Rank_Xanthomonadales_Rank_Xanthomonadaceae_Rank_Pseudoxanthomonas"
#> [246] "st__ASV_26"
#> [247] "s__Bacteria_Rank_Proteobacteria_Rank_Gammaproteobacteria_Rank_Pseudomonadales_Rank_Pseudomonadaceae_Rank_Pseudomonas_Rank_Unassigned"
#> [248] "st__ASV_90"
#> [249] "st__ASV_13"
#> [250] "st__ASV_7"
#> [251] "s__Bacteria_Rank_Proteobacteria_Rank_Gammaproteobacteria_Rank_Xanthomonadales_Rank_Xanthomonadaceae_Rank_Pseudoxanthomonas_Rank_Pseudoxanthomonas_wuyuanensis"
#> [252] "st__ASV_80"
#> [253] "st__ASV_110"
#> [254] "g__Bacteria_Rank_Proteobacteria_Rank_Alphaproteobacteria_Rank_Sphingomonadales_Rank_Sphingomonadaceae_Rank_Sphingopyxis"
#> [255] "s__Bacteria_Rank_Proteobacteria_Rank_Alphaproteobacteria_Rank_Sphingomonadales_Rank_Sphingomonadaceae_Rank_Sphingopyxis_Rank_Sphingopyxis_soli"
#> [256] "st__ASV_71"
#> [257] "st__ASV_68"
#> [258] "s__Bacteria_Rank_Actinobacteria_Rank_Actinobacteria_Rank_Actinomycetales_Rank_Pseudonocardiaceae_Rank_Actinophytocola_Rank_Actinophytocola_burenkhanensis"
#> [259] "g__Bacteria_Rank_Actinobacteria_Rank_Actinobacteria_Rank_Actinomycetales_Rank_Micromonosporaceae_Rank_Actinoplanes"
#> [260] "s__Bacteria_Rank_Actinobacteria_Rank_Actinobacteria_Rank_Actinomycetales_Rank_Micromonosporaceae_Rank_Actinoplanes_Rank_Unassigned"
#> [261] "s__Bacteria_Rank_Actinobacteria_Rank_Actinobacteria_Rank_Actinomycetales_Rank_Pseudonocardiaceae_Rank_Actinophytocola_Rank_Unassigned"
#> [262] "st__ASV_92"
#> [263] "s__Bacteria_Rank_Bacteroidetes_Rank_Flavobacteriia_Rank_Flavobacteriales_Rank_Flavobacteriaceae_Rank_Flavobacterium_Rank_Unassigned"
#> [264] "g__Bacteria_Rank_Actinobacteria_Rank_Actinobacteria_Rank_Actinomycetales_Rank_Pseudonocardiaceae_Rank_Kibdelosporangium"
#> [265] "s__Bacteria_Rank_Actinobacteria_Rank_Actinobacteria_Rank_Actinomycetales_Rank_Pseudonocardiaceae_Rank_Kibdelosporangium_Rank_Kibdelosporangium_phytohabitans"
#> [266] "st__ASV_69"
#> [267] "st__ASV_31"
#> [268] "st__ASV_9"
#> [269] "f__Bacteria_Rank_Actinobacteria_Rank_Actinobacteria_Rank_Actinomycetales_Rank_Microbacteriaceae"
#> [270] "g__Bacteria_Rank_Actinobacteria_Rank_Actinobacteria_Rank_Actinomycetales_Rank_Microbacteriaceae_Rank_Agromyces"
#> [271] "s__Bacteria_Rank_Actinobacteria_Rank_Actinobacteria_Rank_Actinomycetales_Rank_Microbacteriaceae_Rank_Agromyces_Rank_Unassigned"
#> [272] "st__ASV_53"
#> [273] "g__Bacteria_Rank_Actinobacteria_Rank_Actinobacteria_Rank_Actinomycetales_Rank_Pseudonocardiaceae_Rank_Actinophytocola"
#> [274] "g__Bacteria_Rank_Proteobacteria_Rank_Betaproteobacteria_Rank_Burkholderiales_Rank_Comamonadaceae_Rank_Hydrogenophaga"
#> [275] "s__Bacteria_Rank_Proteobacteria_Rank_Betaproteobacteria_Rank_Burkholderiales_Rank_Comamonadaceae_Rank_Hydrogenophaga_Rank_Hydrogenophaga_intermedia"
#> [276] "st__ASV_100"
#> [277] "st__ASV_19"
#> [278] "p__Bacteria_Rank_Bacteroidetes"
#> [279] "c__Bacteria_Rank_Bacteroidetes_Rank_Flavobacteriia"
#> [280] "o__Bacteria_Rank_Bacteroidetes_Rank_Flavobacteriia_Rank_Flavobacteriales"
#> [281] "f__Bacteria_Rank_Bacteroidetes_Rank_Flavobacteriia_Rank_Flavobacteriales_Rank_Flavobacteriaceae"
#> [282] "g__Bacteria_Rank_Bacteroidetes_Rank_Flavobacteriia_Rank_Flavobacteriales_Rank_Flavobacteriaceae_Rank_Flavobacterium"
#> [283] "st__ASV_27"
#> [284] "st__ASV_84"
ldamean$Pvalues <- signif(rawpvalues[match(row.names(ldamean),names(rawpvalues))],digits=5)
ldamean$FDR <- signif(clapvalues,digits=5)
resTable <- ldamean
rawNms <- rownames(resTable);
rownames(resTable) <- gsub("`", '', rawNms);
if (adjust.p) {
de.Num <- sum(clapvalues <= p.lvl & ldamean$LDAscore>=lda.lvl)
} else {
de.Num <- sum(rawpvalues <= p.lvl & ldamean$LDAscore>=lda.lvl)
}
if(de.Num == 0){
current.msg <<- "No significant features were identified with given criteria.";
}else{
current.msg <<- paste("A total of", de.Num, "significant features with given criteria.")
}
print(current.msg)
#> [1] "A total of 129 significant features with given criteria."
ord.inx <- order(resTable$Pvalues, resTable$LDAscore)
resTable <- resTable[ord.inx, ,drop=FALSE]
resTable <- resTable[,c(ncol(resTable),1:(ncol(resTable)-1))]
resTable <- resTable[,c(ncol(resTable),1:(ncol(resTable)-1))]
ldamean$Pvalues[is.na(ldamean$Pvalues)] = 1
if (adjust.p) {
taxtree = resTable[clapvalues <=p.lvl & ldamean$LDAscore>=lda.lvl,]
} else {
# taxtree = resTable[ldamean$Pvalues <=p.lvl & ldamean$LDAscore>=lda.lvl,]
taxtree = resTable[ldamean$Pvalues <=p.lvl,]
}
colour = c('darkgreen','red',"blue","#4DAF4A", "#984EA3", "#FF7F00", "#FFFF33", "#A65628", "#F781BF")
selececol = colour[1:length(levels(as.factor(taxtree$class)))]
names(selececol) = levels(as.factor(taxtree$class))
A = rep("a",length(row.names(taxtree)))
for (i in 1:length(row.names(taxtree))) {
A[i] = selececol [taxtree$class[i]]
}
taxtree$color = A
lefse_lists = data.frame(node=row.names(taxtree),
color=A,
Group = taxtree$class,
stringsAsFactors = FALSE)
taxtree$ID = row.names(taxtree)
head(taxtree)taxtree$ID = gsub("_Rank_",";",taxtree$ID)
taxtree <- taxtree %>%
arrange(class,LDAscore)
taxtree$ID = factor(taxtree$ID,levels=taxtree$ID)
taxtree$class = factor(taxtree$class,levels = unique(taxtree$class))
pbar <- ggplot(taxtree) + geom_bar(aes(y =ID, x = LDAscore,fill = class),stat = "identity") +
scale_fill_manual(values = unique(taxtree$color)) +
scale_x_continuous(limits = c(0,max(taxtree$LDAscore)*1.2))
pbar
Code 5B（Example 28）
count =ps %>%
ggClusterNet::vegan_otu() %>% t()
count[is.na(count)] = 0
norm = t(t(count)/colSums(count,na=TRUE))# * 100 # normalization to total 100
otu.pca <- stats::prcomp(t(norm), scale. = TRUE)
yangpin<-otu.pca$x
yangpin=as.data.frame(yangpin)
yangpin$SampleType= phyloseq::sample_data(ps)$Group
bianliang<-otu.pca$rotation
bianliang=as.data.frame(bianliang)
head(bianliang)dim(norm)
#> [1] 2861 18
index = merge(norm ,bianliang, by="row.names",all=F)
head(index)row.names(index)=index$Row.names
index$Row.names=NULL
head(index)
index$PCone = index$PC1^2
top = index %>% arrange(desc(PCone)) %>%
head(20)
top$ID = row.names(top)
head(top)
p=ggplot(top, aes(x = PCone, y = reorder(ID,PCone))) +
geom_segment(aes(yend=ID),xend=0,size=3,colour = "#1B9E77" )+
geom_point(size=4,pch=20, colour = "#1B9E77")+theme_bw()+
theme(axis.text.x = element_text(colour = "black",size = 20,face = "bold"),
axis.text.y = element_text(colour = "black",size = 10,face = "bold"))
p
Code 5C（Example 30）
group = "Group"
optimal = 20
rfcv = FALSE
ps = ps %>% ggClusterNet::scale_micro()
map = as.data.frame(phyloseq::sample_data(ps))
#-scaleing relative abundancce#----
mapping = as.data.frame(phyloseq::sample_data(ps))
otutab = as.data.frame((ggClusterNet::vegan_otu(ps)))
tem = colnames(otutab)
colnames(otutab) = paste("RE",1:length(colnames(otutab)),sep = "")
tem2 = data.frame(ID = colnames(otutab),name = tem)
otutab$group = factor(mapping$Group)
# colnames(otutab) <- gsub("-","_",colnames(otutab))
model_rf= randomForest::randomForest(group ~ ., data=otutab, importance=TRUE, proximity=TRUE)
print(model_rf)
#>
#> Call:
#> randomForest(formula = group ~ ., data = otutab, importance = TRUE, proximity = TRUE)
#> Type of random forest: classification
#> Number of trees: 500
#> No. of variables tried at each split: 53
#>
#> OOB estimate of error rate: 16.67%
#> Confusion matrix:
#> KO OE WT class.error
#> KO 6 0 0 0.0000000
#> OE 0 4 2 0.3333333
#> WT 0 1 5 0.1666667
Confusion_matrix <- as.data.frame(model_rf$confusion)
Confusion_matrix$class.error <- round(Confusion_matrix$class.error,3)
Confusion_matrix$Group = row.names(Confusion_matrix)
Confusion_matrix <- dplyr::select(Confusion_matrix , Group, everything())
model_Accuracy_rates <- paste(round(100-tail(model_rf$err.rate[,1],1)*100,2),"%",sep = "")
model_Accuracy_rates = data.frame(ID = "model Accuracy rates",model_Accuracy_rates = model_Accuracy_rates)
colnames(model_Accuracy_rates) = c("Random foreest","Fu wilt model")
tab2 <- ggpubr::ggtexttable(Confusion_matrix, rows = NULL)
tab1 <- ggpubr::ggtexttable(model_Accuracy_rates, rows = NULL)
library(patchwork)
pn <- tab1/tab2
if (rfcv) {
result = Micro.rfcv(otu = NULL,tax = NULL,map = NULL,tree = NULL ,ps = ps_rela,group = "Group",optimal = 20,nrfcvnum = 6)
prfcv = result[[1]]# plot rfcv
# result[[2]]# plotdata
rfcvtable = result[[3]]# table rfcv
} else{
prfcv = NULL
rfcvtable = NULL
}
a=as.data.frame(round(randomForest::importance(model_rf), 2))
a$id=row.names(a)
head(a)
row.names(a) = tem
a$id = tem
a2<- dplyr::arrange(a, desc(MeanDecreaseAccuracy)) %>% as.data.frame()
row.names(a2)=a2$id
# optimal = 40
a3=head(a2,n=optimal)
OTU = ggClusterNet::vegan_otu(ps)
### pice mapping
design = as.data.frame(phyloseq::sample_data(ps))
#mean abundance by groups
iris.split <- split(as.data.frame(OTU),as.factor(design$Group))
iris.apply <- lapply(iris.split,function(x)colMeans(x,na.rm = TRUE))
norm2 <- do.call(rbind,iris.apply)%>% # combine result
t()
colnames(norm2) = paste(colnames(norm2),"mean",sep = "")
ind_fal = merge(a3,norm2,by = "row.names",all = F)
head(ind_fal)
head(a3)p1 <- ggplot(a3, aes(x = MeanDecreaseAccuracy, y = reorder(id,MeanDecreaseAccuracy))) +
geom_point(size=6,pch=21,fill = "#9ACD32",color = "#9ACD32")+
geom_segment(aes(yend=id),xend=0,size=3,color = "#9ACD32")+
geom_label(aes(x =MeanDecreaseAccuracy*1.1, label = id),size = 3) + theme_classic()
p1
a3<- dplyr::arrange(a3, desc(MeanDecreaseAccuracy))
a3$iid = paste(1:length(a3$id))
angle1 = 90 - 360 * ( as.numeric(a3$iid) - 0.5) /length(a3$id)
a3$id = factor(a3$id,levels = a3$id)
p2 = a3 %>%
ggplot(aes(x = factor(id), y = MeanDecreaseAccuracy ,label = id)) +
geom_bar(stat = 'identity', position = 'dodge',fill = "blue") +
# scale_fill_manual(values = mi)+
geom_text(hjust = 0, angle = angle1, alpha = 1) +
coord_polar() +
# ylim(c(min,max))+
theme_void()
p2
Code 5D（Example 31）
# BiocManager::install("ROCR")
data(ps)
ps.1 = subset_samples.wt(ps,"Group",c("WT"),TRUE) %>%
filter_OTU_ps(500)
library(randomForest)
library(caret)
library(ROCR)
library(e1071)
MicroRoc <- function(otu = NULL,tax = NULL,map = NULL,tree = NULL,
ps = NULL,group = "Group",repnum = 5){
ps = ggClusterNet::inputMicro(otu,tax,map,tree,ps,group = group)
mapping = as.data.frame(phyloseq::sample_data(ps))
otutab = as.data.frame(t(ggClusterNet::vegan_otu(ps)))
colnames(otutab) <- gsub("-","_",colnames(otutab))
test = as.data.frame(t(otutab))
test$group = factor(mapping$Group)
colnames(test) = paste("OTU",colnames(test),sep = "")
# random forest
test = dplyr::select(test,OTUgroup,everything())
train = test
folds <- createFolds(y=test[,1],k=repnum)
AUC =c()
max=0
num=0
fc<-as.numeric()
mod_pre<-as.numeric()
for(i in 1:repnum){
fold_test<-train[folds[[i]],]
fold_train<-train[-folds[[i]],]
colnames(fold_test) <- gsub("-","_",colnames(fold_test))
colnames(fold_train) <- gsub("-","_",colnames(fold_train))
model<-randomForest(OTUgroup~.,data=fold_train, importance=TRUE, proximity=TRUE)
model_pre<-predict(model,newdata = fold_test,type="prob")
fc<-append(fc,as.factor(fold_test$OTUgroup))
mod_pre<-append(mod_pre,model_pre[,2])
}
#- pick data and plot
pred <- prediction(mod_pre, fc)
perf <- performance(pred,"tpr","fpr")
x <- unlist(perf@x.values)
y <- unlist(perf@y.values)
plotdata <- data.frame(x,y)
names(plotdata) <- c("x", "y")
AUC[1] = paste("rf AUC:",round(performance(pred,'auc')@y.values[[1]],3),sep = " ")
head(plotdata)
g0 <- ggplot(plotdata) +
geom_path(aes(x = x, y = y, colour = x), size=1,color = "red") +
labs(x = "False positive rate", y = "Ture positive rate") + # , title ="Random Forest"
annotate("text", x=0.75, y=0.5, label=paste("Red: ",AUC[1],sep = ""))
df<-cbind(fc,as.numeric(mod_pre))
#-svm
max=0
num=0
fc<-as.numeric()
mod_pre<-as.numeric()
for(i in 1:repnum){
fold_test<-train[folds[[i]],]
# head(fold_test)
fold_train<-train[-folds[[i]],]
model<-svm(OTUgroup~.,data=fold_train,probability=TRUE)
model
model_pre<-predict(model,newdata = fold_test,decision.values = TRUE, probability = TRUE)
fc<-append(fc,as.numeric(fold_test$OTUgroup))
mod_pre<-append(mod_pre,as.numeric(attr(model_pre, "probabilities")[,2]))
}
pred <- prediction(mod_pre, fc)
perf <- performance(pred,"tpr","fpr")
x <- unlist(perf@x.values)
y <- unlist(perf@y.values)
plotdata <- data.frame(x,y)
names(plotdata) <- c("x", "y")
AUC[2] = paste("svm AUC:",round(performance(pred,'auc')@y.values[[1]],3),sep = " ")
g1 <- g0 +
geom_path(data = plotdata,aes(x = x, y = y, colour = x), size=1,color = "blue") +
# labs(x = "False positive rate", y = "Ture positive rate") +
annotate("text", x=0.75, y=0.4, label=paste("Blue: ",AUC[2],sep = ""))
df<-cbind(df,cbind(fc,mod_pre))
#GLM
max=0
num=0
fc<-as.numeric()
mod_pre<-as.numeric()
for(i in 1:repnum){
fold_test<-train[folds[[i]],]
fold_train<-train[-folds[[i]],]
model<-glm(OTUgroup~.,family='binomial',data=fold_train)
model
model_pre<-predict(model,type='response',newdata=fold_test)
model_pre
fc<-append(fc,fold_test$OTUgroup)
mod_pre<-append(mod_pre,as.numeric(model_pre))
}
pred <- prediction(mod_pre, fc)
perf <- performance(pred,"tpr","fpr")
x <- unlist(perf@x.values)
y <- unlist(perf@y.values)
plotdata <- data.frame(x,y)
names(plotdata) <- c("x", "y")
AUC[3] = paste("GLM AUC:",round(performance(pred,'auc')@y.values[[1]],3),sep = " ")
g2 <- g1 +
geom_path(data = plotdata,aes(x = x, y = y, colour = x), size=1,color = "black") +
labs(x = "False positive rate", y = "Ture positive rate") +
annotate("text", x=0.75, y=0.3, label=paste("Black: ",AUC[3],sep = ""))
g2
df<-cbind(df,cbind(fc,mod_pre))
return(list(g2,AUC,df))
}
result = MicroRoc( ps = ps.1,group = "Group")
p <- result[[1]]
p
Correction and network analysis
Code 6A（Example 32）
# devtools::install_github("zdk123/SpiecEasi")
library(psych)
ps_sub = filter_OTU_ps(ps = ps,Top = 150)
otu_table = as.data.frame(t(vegan_otu(ps_sub)))
head(otu_table)occor = psych::corr.test(t(otu_table),use="pairwise",
adjust="fdr",alpha=.05)
occor.r = occor$r
occor.p = occor$p
result <- sparcc.micro(data = t(otu_table),R = 10,ncpus = 1)
occor.r = result[[1]]
occor.p = result[[2]]
occor.p
#> ASV_102 ASV_136 ASV_75 ASV_110 ASV_3 ASV_55 ASV_116 ASV_40 ASV_4
#> ASV_102 1.0 0.0 0.0 0.0 0.2 0.0 0.2 0.2 1.0
#> ASV_136 0.0 1.0 0.0 0.0 0.0 0.0 0.4 0.6 0.6
#> ASV_75 0.0 0.0 1.0 0.6 0.6 0.4 0.2 0.0 0.6
#> ASV_110 0.0 0.0 0.6 1.0 0.0 0.0 0.2 0.0 0.6
#> ASV_3 0.2 0.0 0.6 0.0 1.0 0.0 0.0 0.6 0.2
#> ASV_55 0.0 0.0 0.4 0.0 0.0 1.0 0.0 0.2 1.0
#> ASV_116 0.2 0.4 0.2 0.2 0.0 0.0 1.0 0.0 0.0
#> ASV_40 0.2 0.6 0.0 0.0 0.6 0.2 0.0 1.0 0.0
#> ASV_4 1.0 0.6 0.6 0.6 0.2 1.0 0.0 0.0 1.0
#> ASV_144 0.8 0.2 0.6 0.6 0.2 0.2 0.4 0.4 0.0
#> ASV_11 0.0 0.0 0.4 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_61 0.0 0.2 0.2 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_94 0.0 0.0 0.0 0.0 0.4 0.0 0.0 0.0 0.0
#> ASV_57 0.0 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_129 0.4 0.6 0.6 0.2 0.0 0.2 0.2 0.4 0.2
#> ASV_93 0.8 0.6 0.2 0.2 0.0 0.0 0.2 0.0 0.4
#> ASV_7 0.2 0.4 0.0 0.0 0.2 0.0 0.0 0.2 0.2
#> ASV_13 0.2 0.6 0.4 0.0 0.0 0.0 0.0 0.0 0.4
#> ASV_12 0.2 0.8 0.0 0.2 0.0 0.0 0.8 0.2 0.6
#> ASV_105 0.0 0.4 0.0 0.4 0.2 0.4 0.0 0.2 0.4
#> ASV_71 0.4 0.2 0.4 0.0 0.8 0.8 0.0 0.0 0.0
#> ASV_48 0.4 0.4 0.2 0.8 0.8 0.6 0.6 0.8 1.0
#> ASV_90 0.8 0.4 1.0 0.0 0.8 0.4 0.2 0.2 0.4
#> ASV_108 0.8 0.0 0.0 0.8 0.0 0.8 0.4 0.2 0.4
#> ASV_100 0.2 0.0 0.0 0.4 0.8 0.8 0.4 0.4 0.2
#> ASV_80 0.2 0.0 0.2 0.4 0.6 0.8 0.2 0.2 0.0
#> ASV_127 0.2 0.6 0.4 0.2 0.8 0.8 0.6 0.4 0.6
#> ASV_20 0.2 0.0 0.2 0.6 0.8 0.6 0.0 0.0 0.0
#> ASV_69 0.2 0.2 0.6 1.0 0.0 0.0 0.2 0.0 0.6
#> ASV_28 0.4 0.6 1.0 1.0 0.8 0.6 0.8 0.0 0.2
#> ASV_92 0.6 0.8 0.6 0.8 0.6 0.8 0.4 0.8 0.0
#> ASV_150 0.2 0.2 0.2 1.0 0.0 0.0 0.0 0.8 0.8
#> ASV_72 0.6 0.0 0.4 0.4 0.2 0.2 0.0 0.4 0.4
#> ASV_76 0.4 0.4 0.4 0.0 0.2 0.2 0.0 0.0 1.0
#> ASV_30 0.2 0.4 0.2 0.2 0.0 0.2 1.0 0.4 0.4
#> ASV_83 0.6 0.8 0.8 0.8 0.0 0.8 0.4 0.4 0.2
#> ASV_50 1.0 0.6 0.4 0.0 0.0 0.6 0.2 0.6 0.0
#> ASV_99 0.0 0.4 0.4 0.8 0.0 0.0 0.2 0.6 0.4
#> ASV_36 0.6 0.2 0.8 0.4 0.0 0.4 0.8 0.4 0.2
#> ASV_38 0.0 0.2 0.4 0.4 0.0 0.2 0.4 0.8 0.4
#> ASV_35 1.0 0.4 0.6 0.0 0.0 0.0 1.0 0.6 1.0
#> ASV_81 0.6 0.0 0.8 0.0 0.0 0.0 1.0 0.0 0.4
#> ASV_114 0.2 0.6 0.8 0.0 0.0 0.2 0.2 0.6 0.0
#> ASV_14 0.2 0.0 0.4 0.0 0.0 0.0 0.6 0.6 0.6
#> ASV_166 0.2 0.6 0.6 0.0 0.0 0.0 0.0 1.0 0.0
#> ASV_123 0.2 0.4 0.6 0.4 0.6 0.2 0.2 0.0 0.4
#> ASV_104 0.4 0.2 0.6 0.4 0.0 0.0 0.2 0.8 0.8
#> ASV_85 1.0 0.0 0.6 0.2 0.8 0.2 1.0 0.4 1.0
#> ASV_98 0.8 0.0 1.0 0.8 0.8 0.0 0.2 0.6 0.0
#> ASV_134 0.8 0.8 0.4 0.8 0.0 0.4 0.2 0.2 0.6
#> ASV_64 0.8 0.8 0.6 0.0 0.0 0.0 0.4 0.4 0.2
#> ASV_141 0.8 0.2 0.0 0.6 0.6 0.0 0.6 0.4 0.0
#> ASV_56 0.4 1.0 0.0 0.6 0.0 0.0 0.2 0.8 0.0
#> ASV_60 0.2 0.8 0.2 0.4 0.6 0.0 0.2 0.2 0.0
#> ASV_2 0.8 0.6 0.0 0.4 0.6 0.2 0.2 0.8 0.0
#> ASV_6 0.4 0.8 1.0 0.8 1.0 0.0 0.4 0.4 0.2
#> ASV_39 0.8 0.4 1.0 1.0 0.2 0.4 0.4 0.2 0.0
#> ASV_44 0.6 0.6 0.0 0.2 0.0 0.2 0.2 0.6 0.0
#> ASV_10 0.6 0.8 0.2 0.2 0.0 0.2 0.2 0.4 0.0
#> ASV_62 0.6 0.4 0.6 0.4 0.2 0.8 0.6 0.8 0.2
#> ASV_96 0.4 0.6 0.2 0.4 0.6 0.4 0.8 0.2 0.2
#> ASV_24 0.8 0.4 0.4 0.4 0.4 0.0 0.6 0.0 0.4
#> ASV_47 0.2 0.4 0.8 0.8 0.6 0.4 0.8 0.4 0.8
#> ASV_63 0.8 1.0 0.0 0.8 0.8 0.0 0.4 0.6 0.2
#> ASV_79 0.4 0.2 0.8 0.8 0.8 0.0 1.0 0.0 0.4
#> ASV_17 0.8 0.6 0.6 0.6 0.6 0.4 0.6 0.0 0.0
#> ASV_41 0.8 1.0 0.8 0.8 0.6 0.0 0.4 0.4 0.2
#> ASV_73 0.4 0.2 0.2 0.6 0.6 0.4 0.4 0.0 0.2
#> ASV_120 0.2 1.0 0.8 1.0 0.4 0.0 0.6 0.0 0.0
#> ASV_137 0.4 0.4 1.0 0.6 0.6 0.0 0.4 0.0 0.2
#> ASV_130 0.8 0.6 0.4 0.0 0.0 0.4 0.0 0.0 0.0
#> ASV_107 0.6 0.8 0.6 0.2 0.2 0.4 0.4 0.0 0.2
#> ASV_115 0.2 0.2 0.2 0.0 0.0 0.0 1.0 0.2 0.2
#> ASV_118 0.0 1.0 0.6 0.8 0.2 1.0 0.8 1.0 0.6
#> ASV_70 0.6 1.0 1.0 0.4 0.8 0.8 0.6 0.2 0.8
#> ASV_103 1.0 1.0 1.0 0.8 0.8 0.2 0.0 0.8 0.4
#> ASV_133 0.2 0.2 0.4 0.4 0.6 0.2 0.8 0.0 0.4
#> ASV_23 0.8 0.6 0.6 0.4 0.8 0.6 1.0 0.4 0.2
#> ASV_42 0.0 0.6 1.0 0.6 0.4 1.0 1.0 0.6 0.0
#> ASV_45 0.0 0.8 0.2 1.0 0.8 1.0 0.8 0.4 1.0
#> ASV_140 0.2 0.4 0.6 0.0 0.2 0.6 0.6 0.8 0.0
#> ASV_97 0.8 0.6 0.6 0.4 0.2 0.6 0.2 0.6 0.0
#> ASV_29 0.4 0.6 0.6 0.0 0.0 0.0 0.0 0.2 0.0
#> ASV_147 0.0 0.8 0.8 0.0 0.2 0.0 0.8 0.6 0.6
#> ASV_54 0.8 0.0 0.8 0.6 0.0 0.0 0.0 0.0 0.8
#> ASV_15 0.4 0.4 0.2 0.0 0.0 0.0 0.8 1.0 0.6
#> ASV_156 0.4 0.2 0.8 0.4 0.0 0.0 0.8 0.2 0.2
#> ASV_132 1.0 0.4 0.0 0.0 0.4 0.4 0.6 0.8 0.8
#> ASV_22 0.0 0.6 0.4 0.6 0.4 0.0 1.0 0.2 0.8
#> ASV_68 0.2 1.0 0.2 1.0 0.2 0.4 0.2 0.0 0.4
#> ASV_59 0.8 0.8 0.0 0.0 0.4 0.0 0.0 0.6 0.4
#> ASV_87 0.4 0.8 0.8 0.2 0.4 0.8 0.0 0.6 0.4
#> ASV_74 0.2 0.8 0.2 0.0 0.2 0.2 0.0 0.2 0.6
#> ASV_65 0.2 0.2 0.8 0.0 0.2 1.0 0.2 1.0 0.8
#> ASV_66 1.0 0.8 0.6 0.2 0.0 0.6 0.8 0.2 0.0
#> ASV_101 1.0 0.6 0.6 0.0 0.0 0.0 0.8 0.8 0.4
#> ASV_122 0.4 0.0 0.2 0.0 0.0 0.0 0.4 0.4 0.0
#> ASV_163 0.8 0.6 0.6 0.6 0.0 0.8 0.8 0.2 0.0
#> ASV_32 0.6 0.4 0.6 0.6 1.0 0.4 0.4 0.8 0.6
#> ASV_128 0.6 0.4 0.8 0.4 0.2 0.6 0.4 0.2 0.0
#> ASV_33 0.8 0.6 0.0 0.2 0.4 0.0 0.2 0.4 0.0
#> ASV_77 0.4 0.8 0.2 0.0 0.0 0.2 0.0 0.8 0.0
#> ASV_159 1.0 0.2 0.0 0.2 0.6 0.4 0.6 0.8 0.6
#> ASV_46 0.4 0.8 0.6 0.8 0.8 0.0 0.2 1.0 0.4
#> ASV_112 0.6 0.0 0.6 1.0 0.0 0.0 0.4 0.8 0.4
#> ASV_89 0.8 0.2 0.4 0.8 0.6 0.0 0.2 1.0 0.0
#> ASV_111 0.2 0.2 0.2 0.6 0.4 0.0 0.6 0.8 0.6
#> ASV_37 0.0 0.0 0.2 0.6 0.0 0.0 0.8 0.4 0.0
#> ASV_1 0.8 0.8 0.8 0.8 0.8 0.2 0.8 0.0 0.6
#> ASV_86 0.8 0.4 0.0 0.8 1.0 1.0 0.2 0.0 0.4
#> ASV_18 0.0 0.6 0.8 1.0 0.6 0.2 0.6 0.0 0.2
#> ASV_34 0.4 0.8 1.0 0.8 0.8 0.0 0.4 0.4 0.6
#> ASV_25 0.6 0.6 1.0 0.6 0.4 0.2 0.2 1.0 0.2
#> ASV_124 0.2 0.4 0.8 0.8 1.0 0.0 0.0 0.4 0.8
#> ASV_119 0.4 0.8 0.6 0.8 0.2 0.0 0.2 1.0 0.8
#> ASV_153 0.8 0.4 0.8 0.6 0.4 0.0 0.0 0.2 0.0
#> ASV_52 0.6 0.8 0.8 0.6 1.0 0.6 0.8 1.0 0.2
#> ASV_21 0.4 0.8 0.2 0.8 0.8 0.8 0.6 0.2 0.2
#> ASV_49 0.6 0.6 0.6 0.0 0.2 0.6 0.8 0.6 0.2
#> ASV_53 0.8 0.6 0.8 0.8 0.2 0.6 0.2 0.8 0.0
#> ASV_138 0.0 0.0 0.0 0.4 1.0 0.4 0.6 0.8 0.8
#> ASV_16 0.0 0.8 0.8 0.6 0.0 0.2 0.8 0.8 0.6
#> ASV_95 0.2 0.0 0.0 0.6 0.6 0.2 0.6 0.6 0.4
#> ASV_9 0.2 0.0 0.4 0.4 0.6 0.6 0.8 1.0 0.2
#> ASV_135 0.2 0.0 0.4 0.2 0.2 1.0 0.4 0.4 0.2
#> ASV_31 0.2 0.2 0.2 0.4 0.6 0.4 0.4 0.0 0.4
#> ASV_91 0.0 0.4 0.0 0.2 0.0 0.0 0.0 0.6 0.2
#> ASV_67 0.6 0.6 0.8 1.0 0.2 0.8 0.6 0.0 0.8
#> ASV_43 0.6 0.8 0.8 0.0 0.0 0.0 0.4 0.4 0.0
#> ASV_172 0.4 0.6 1.0 0.0 0.0 0.0 0.2 0.4 0.2
#> ASV_51 0.0 0.4 0.2 0.0 0.4 0.6 1.0 0.0 0.2
#> ASV_117 0.0 0.2 0.2 0.0 0.2 0.2 0.0 0.4 0.2
#> ASV_8 0.2 0.8 0.8 0.6 0.2 0.0 0.0 0.2 0.2
#> ASV_106 0.0 0.2 0.8 0.4 0.6 0.4 0.4 0.8 0.4
#> ASV_125 1.0 0.0 0.0 1.0 0.4 0.8 0.2 0.8 0.0
#> ASV_113 0.2 0.4 0.0 0.6 0.2 0.8 0.8 0.6 0.0
#> ASV_109 1.0 0.6 0.6 0.0 0.0 0.8 0.0 0.8 0.2
#> ASV_26 0.6 0.0 0.4 0.2 0.2 0.0 0.2 1.0 0.0
#> ASV_154 0.0 0.6 0.2 0.6 0.4 0.4 0.4 0.4 0.2
#> ASV_88 0.2 0.4 0.4 0.0 0.0 0.0 0.0 0.4 0.2
#> ASV_151 0.6 0.6 0.4 0.0 0.0 0.0 0.2 0.2 1.0
#> ASV_27 0.0 0.2 1.0 0.0 0.0 0.0 0.2 0.0 0.0
#> ASV_148 0.6 0.0 0.4 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_162 0.6 0.0 0.4 0.0 0.0 0.0 0.2 0.0 0.0
#> ASV_142 0.4 0.4 0.4 0.0 0.0 0.2 0.0 0.0 0.0
#> ASV_121 0.0 0.2 0.4 0.0 0.0 0.2 0.4 1.0 0.2
#> ASV_126 0.8 0.6 0.8 0.0 0.0 1.0 0.2 1.0 0.4
#> ASV_78 0.2 0.4 0.0 0.8 0.0 0.0 0.6 0.8 0.6
#> ASV_19 0.4 0.4 0.8 0.2 0.0 0.0 0.0 0.4 0.0
#> ASV_84 0.0 0.2 0.4 0.0 0.4 0.4 0.8 0.8 0.4
#> ASV_144 ASV_11 ASV_61 ASV_94 ASV_57 ASV_129 ASV_93 ASV_7 ASV_13 ASV_12
#> ASV_102 0.8 0.0 0.0 0.0 0.0 0.4 0.8 0.2 0.2 0.2
#> ASV_136 0.2 0.0 0.2 0.0 0.0 0.6 0.6 0.4 0.6 0.8
#> ASV_75 0.6 0.4 0.2 0.0 0.2 0.6 0.2 0.0 0.4 0.0
#> ASV_110 0.6 0.0 0.0 0.0 0.0 0.2 0.2 0.0 0.0 0.2
#> ASV_3 0.2 0.0 0.0 0.4 0.0 0.0 0.0 0.2 0.0 0.0
#> ASV_55 0.2 0.0 0.0 0.0 0.0 0.2 0.0 0.0 0.0 0.0
#> ASV_116 0.4 0.0 0.0 0.0 0.0 0.2 0.2 0.0 0.0 0.8
#> ASV_40 0.4 0.0 0.0 0.0 0.0 0.4 0.0 0.2 0.0 0.2
#> ASV_4 0.0 0.0 0.0 0.0 0.0 0.2 0.4 0.2 0.4 0.6
#> ASV_144 1.0 0.2 0.2 0.0 0.4 0.4 1.0 0.0 0.6 1.0
#> ASV_11 0.2 1.0 0.0 0.0 0.0 0.4 0.6 0.0 0.6 0.6
#> ASV_61 0.2 0.0 1.0 0.0 0.0 0.8 0.6 0.0 1.0 0.6
#> ASV_94 0.0 0.0 0.0 1.0 0.0 0.2 0.0 0.0 0.0 1.0
#> ASV_57 0.4 0.0 0.0 0.0 1.0 0.2 0.2 0.0 0.0 0.2
#> ASV_129 0.4 0.4 0.8 0.2 0.2 1.0 0.0 0.0 0.0 0.0
#> ASV_93 1.0 0.6 0.6 0.0 0.2 0.0 1.0 0.0 0.0 0.0
#> ASV_7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0
#> ASV_13 0.6 0.6 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0
#> ASV_12 1.0 0.6 0.6 1.0 0.2 0.0 0.0 0.0 0.0 1.0
#> ASV_105 0.8 0.4 0.4 0.0 0.4 0.0 0.0 0.8 0.2 0.6
#> ASV_71 0.2 0.0 0.0 0.0 0.4 0.0 0.0 1.0 0.4 1.0
#> ASV_48 0.2 0.2 0.2 0.4 0.6 0.0 0.0 0.8 0.0 0.0
#> ASV_90 0.0 0.2 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_108 0.0 0.6 0.6 0.8 1.0 0.4 0.4 0.6 0.8 0.0
#> ASV_100 0.0 0.4 0.4 0.0 0.0 0.4 0.6 0.4 0.6 0.4
#> ASV_80 0.0 0.2 1.0 0.0 0.0 0.8 0.8 0.6 0.0 0.8
#> ASV_127 0.0 0.4 0.8 0.4 0.6 1.0 0.6 0.2 0.0 0.2
#> ASV_20 0.2 1.0 0.6 0.2 0.0 0.6 0.4 0.6 0.8 0.6
#> ASV_69 0.2 0.6 0.8 0.0 0.4 0.8 0.4 0.0 0.8 0.0
#> ASV_28 0.8 0.6 1.0 0.6 0.2 1.0 1.0 0.4 0.4 1.0
#> ASV_92 0.4 0.8 0.6 0.2 0.2 0.0 0.8 0.0 0.2 0.8
#> ASV_150 0.0 0.6 0.2 0.4 0.2 0.8 0.2 0.6 0.4 0.0
#> ASV_72 0.2 0.8 0.0 0.6 0.0 0.2 0.8 0.0 0.0 0.2
#> ASV_76 0.0 0.4 0.0 1.0 0.6 0.6 0.8 0.4 0.0 0.8
#> ASV_30 0.0 0.0 0.0 0.8 0.2 0.6 0.2 0.0 0.2 0.0
#> ASV_83 0.4 0.2 1.0 0.8 0.6 0.8 0.8 0.2 0.8 0.0
#> ASV_50 1.0 0.2 0.4 1.0 0.8 0.2 0.8 0.2 0.8 0.0
#> ASV_99 0.0 0.2 0.4 0.0 0.6 0.4 0.6 0.4 0.8 0.0
#> ASV_36 0.0 0.4 1.0 0.6 0.8 0.4 1.0 0.2 0.2 0.4
#> ASV_38 0.0 0.4 0.8 0.4 0.2 0.4 0.4 0.2 0.2 0.4
#> ASV_35 0.0 0.0 0.8 0.4 0.8 0.0 0.0 0.4 0.2 0.6
#> ASV_81 0.2 0.2 0.6 0.4 0.8 0.8 0.4 0.2 0.2 0.0
#> ASV_114 0.2 0.4 0.2 0.0 0.0 0.0 0.0 0.2 0.8 0.0
#> ASV_14 0.6 0.0 0.0 0.8 0.0 0.8 0.8 1.0 0.8 0.0
#> ASV_166 1.0 0.4 0.4 0.0 0.0 0.6 0.0 0.2 0.0 0.0
#> ASV_123 0.2 0.2 1.0 0.8 0.2 0.6 0.0 0.2 0.0 0.0
#> ASV_104 0.8 0.6 0.2 0.6 0.6 0.4 0.4 0.8 0.8 0.0
#> ASV_85 0.2 0.0 0.0 0.6 0.0 0.6 0.6 1.0 0.6 1.0
#> ASV_98 0.4 0.2 0.4 0.8 0.6 0.0 0.2 0.4 0.2 0.8
#> ASV_134 0.2 0.0 0.0 0.2 0.0 0.0 0.2 0.8 0.4 0.6
#> ASV_64 0.6 1.0 0.4 0.8 0.4 0.0 0.0 0.4 0.2 0.8
#> ASV_141 0.4 0.4 0.8 0.8 1.0 0.4 0.0 0.4 0.6 0.2
#> ASV_56 0.8 0.2 0.6 0.2 0.2 0.0 0.8 1.0 0.8 0.0
#> ASV_60 0.8 0.2 0.6 0.4 0.6 0.2 1.0 0.6 0.6 0.0
#> ASV_2 0.8 0.8 0.4 0.6 0.8 0.6 0.8 0.6 0.6 0.2
#> ASV_6 1.0 0.8 0.8 0.4 0.2 0.4 0.4 0.8 0.8 0.0
#> ASV_39 0.6 0.0 0.4 0.2 1.0 0.6 0.4 0.6 0.6 0.0
#> ASV_44 0.6 0.6 0.0 0.4 0.2 0.2 0.4 0.0 0.4 0.2
#> ASV_10 1.0 0.6 0.6 0.8 0.6 0.0 0.4 0.4 0.2 0.8
#> ASV_62 1.0 0.6 0.8 0.4 0.8 0.0 0.2 0.2 0.2 0.4
#> ASV_96 0.8 0.8 1.0 0.4 0.8 0.0 0.8 0.2 0.6 0.4
#> ASV_24 1.0 0.8 0.8 0.8 0.6 0.6 0.8 0.8 0.6 0.2
#> ASV_47 0.2 0.8 0.6 0.8 1.0 0.6 0.6 0.6 0.4 0.0
#> ASV_63 0.0 0.8 0.6 1.0 0.4 0.6 0.2 0.6 0.0 0.0
#> ASV_79 0.2 0.8 0.6 0.6 0.8 0.6 0.0 0.2 0.0 0.0
#> ASV_17 0.6 0.4 0.6 0.8 0.8 0.8 0.0 0.8 0.0 0.0
#> ASV_41 0.6 0.4 0.4 0.8 0.6 0.4 0.6 0.6 0.4 0.0
#> ASV_73 0.6 0.4 0.2 0.2 0.4 0.4 0.4 0.4 0.0 0.0
#> ASV_120 0.4 0.8 0.0 0.2 0.0 0.8 0.2 0.4 0.0 0.0
#> ASV_137 0.2 0.4 0.6 0.2 0.6 0.0 0.6 1.0 0.8 0.8
#> ASV_130 0.0 0.4 1.0 0.2 0.0 0.4 0.0 0.0 0.0 0.0
#> ASV_107 0.0 0.0 0.0 1.0 0.0 0.6 0.0 0.0 0.0 0.0
#> ASV_115 0.2 0.0 0.2 0.0 0.0 0.8 0.0 0.0 0.0 0.0
#> ASV_118 0.6 0.2 0.2 0.0 0.4 0.8 0.4 0.4 0.0 0.0
#> ASV_70 0.4 0.6 0.6 0.6 0.6 0.6 0.2 0.8 0.4 0.2
#> ASV_103 0.6 1.0 1.0 0.8 0.8 1.0 0.6 0.8 0.8 0.0
#> ASV_133 0.8 0.0 0.2 0.2 0.0 0.0 0.0 0.8 0.0 0.0
#> ASV_23 0.0 0.8 0.8 0.6 0.4 1.0 0.2 0.4 0.2 0.0
#> ASV_42 0.0 0.2 0.2 0.4 0.4 0.8 0.4 1.0 0.2 0.6
#> ASV_45 0.2 0.4 0.4 0.4 0.0 0.8 1.0 0.8 0.2 0.2
#> ASV_140 0.6 0.0 0.2 0.2 0.6 0.0 0.4 1.0 0.0 0.0
#> ASV_97 0.4 0.2 0.0 0.2 0.0 0.0 0.4 0.0 0.0 0.6
#> ASV_29 0.8 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_147 0.0 0.4 0.2 0.0 0.0 0.4 0.6 0.0 0.4 0.2
#> ASV_54 0.6 0.4 0.6 0.8 0.8 0.8 0.4 0.4 0.4 0.2
#> ASV_15 0.0 0.0 0.0 1.0 0.4 0.0 0.0 0.2 0.0 0.2
#> ASV_156 0.0 0.4 0.4 0.6 0.8 0.4 0.4 0.0 0.0 0.0
#> ASV_132 0.6 0.4 0.6 0.4 0.2 0.0 0.2 0.0 0.0 0.0
#> ASV_22 1.0 0.8 0.8 0.6 0.6 0.8 0.6 0.6 0.2 0.4
#> ASV_68 0.2 0.2 0.0 0.0 0.0 0.4 1.0 0.4 0.2 0.8
#> ASV_59 0.4 0.8 0.8 0.0 0.6 0.0 0.0 0.2 0.0 0.4
#> ASV_87 0.4 0.4 0.8 0.6 0.8 0.0 0.0 0.2 0.0 0.4
#> ASV_74 0.8 0.0 0.6 0.2 0.2 0.0 0.8 0.6 0.2 0.4
#> ASV_65 0.0 0.6 0.8 0.4 0.6 0.4 0.6 0.6 0.6 0.8
#> ASV_66 0.6 0.4 0.4 0.8 1.0 0.0 0.6 0.2 0.6 0.6
#> ASV_101 0.6 0.2 0.2 0.0 0.4 0.0 0.0 0.0 0.0 0.0
#> ASV_122 1.0 0.0 0.2 0.2 0.2 0.0 0.0 0.8 0.4 0.6
#> ASV_163 0.4 0.0 0.0 0.0 0.4 0.0 0.8 0.4 0.6 0.4
#> ASV_32 0.8 1.0 0.6 0.8 1.0 0.0 1.0 0.0 0.2 0.6
#> ASV_128 0.8 0.0 0.0 0.4 0.2 0.4 1.0 0.0 0.0 0.2
#> ASV_33 0.4 0.6 0.6 0.4 0.6 0.4 0.4 0.0 0.2 0.8
#> ASV_77 0.8 0.4 0.4 0.8 0.2 0.0 0.2 0.0 0.0 0.8
#> ASV_159 0.6 0.8 0.8 0.8 1.0 0.0 0.2 0.2 0.6 1.0
#> ASV_46 0.6 1.0 1.0 0.4 0.8 0.4 0.8 0.2 0.2 0.2
#> ASV_112 0.2 1.0 1.0 0.6 1.0 0.0 1.0 1.0 0.6 0.0
#> ASV_89 0.4 1.0 1.0 0.2 0.6 0.8 0.6 0.0 0.4 0.6
#> ASV_111 0.4 0.8 0.6 0.6 0.8 1.0 0.4 0.6 0.2 0.8
#> ASV_37 0.6 0.2 0.0 0.0 0.0 1.0 0.0 0.2 0.2 0.4
#> ASV_1 0.8 0.6 0.4 0.2 0.4 0.0 0.4 0.8 0.8 0.8
#> ASV_86 0.8 0.6 1.0 0.4 0.6 1.0 0.6 0.4 1.0 0.8
#> ASV_18 0.8 1.0 1.0 0.2 0.6 0.4 0.4 1.0 0.8 0.2
#> ASV_34 0.6 0.6 0.6 0.4 0.4 0.2 0.6 0.8 0.4 0.4
#> ASV_25 0.0 0.6 0.4 0.6 0.6 0.6 0.4 0.8 0.4 0.0
#> ASV_124 0.2 0.6 0.2 0.6 1.0 0.6 0.6 0.2 0.0 0.6
#> ASV_119 1.0 0.8 0.8 0.4 0.4 0.6 0.0 0.6 1.0 0.0
#> ASV_153 0.0 0.8 0.6 0.4 0.8 0.6 0.2 0.6 0.4 0.2
#> ASV_52 0.0 0.6 1.0 1.0 1.0 1.0 0.8 0.4 0.4 0.2
#> ASV_21 0.0 0.8 0.8 0.2 0.6 0.4 0.4 0.8 0.8 0.0
#> ASV_49 0.0 1.0 0.2 0.2 0.8 0.4 0.2 0.0 0.0 0.6
#> ASV_53 0.0 0.6 0.4 0.8 1.0 0.8 1.0 1.0 0.8 0.2
#> ASV_138 0.8 0.6 0.8 0.2 0.6 0.8 0.0 0.2 0.4 0.0
#> ASV_16 0.6 0.0 0.0 0.6 0.4 0.8 0.0 0.2 0.4 0.0
#> ASV_95 0.0 0.4 1.0 0.4 0.8 0.2 0.2 0.0 0.4 0.0
#> ASV_9 0.0 0.0 0.0 0.0 0.0 0.6 0.4 0.8 0.4 0.6
#> ASV_135 0.4 0.4 0.4 0.0 0.4 0.2 1.0 0.6 0.8 0.6
#> ASV_31 0.2 0.4 0.4 0.4 0.2 0.6 0.4 0.4 0.4 0.0
#> ASV_91 0.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.6
#> ASV_67 0.0 0.4 0.6 0.4 0.6 0.0 0.0 0.8 0.6 0.0
#> ASV_43 0.0 0.2 0.2 0.4 0.2 0.0 0.0 0.0 0.2 0.2
#> ASV_172 0.0 0.2 0.2 0.4 0.0 0.0 0.0 0.2 0.2 0.6
#> ASV_51 0.6 1.0 0.6 0.8 0.8 0.4 0.0 0.0 0.6 0.4
#> ASV_117 0.0 0.8 1.0 0.8 0.2 0.2 0.4 0.2 0.4 0.4
#> ASV_8 0.6 0.0 0.4 0.2 0.0 0.0 0.8 1.0 0.4 0.0
#> ASV_106 0.0 0.2 0.4 0.0 0.2 0.0 0.2 0.6 0.6 0.0
#> ASV_125 0.0 0.8 0.8 0.6 0.8 0.8 0.8 0.6 1.0 0.2
#> ASV_113 0.2 1.0 0.4 0.2 0.4 1.0 1.0 0.6 1.0 0.6
#> ASV_109 1.0 0.6 1.0 0.4 0.6 0.0 0.2 0.0 0.0 0.8
#> ASV_26 0.4 1.0 0.6 0.8 1.0 0.0 0.2 0.4 0.2 1.0
#> ASV_154 0.6 0.8 0.4 0.2 0.2 0.0 0.2 0.0 0.2 0.2
#> ASV_88 0.2 0.0 0.2 0.0 0.4 0.0 0.0 0.0 0.0 0.2
#> ASV_151 0.2 0.4 0.6 0.6 0.6 0.0 0.0 0.0 0.0 0.0
#> ASV_27 0.0 0.2 0.8 0.8 0.8 0.0 0.0 0.0 0.0 0.0
#> ASV_148 0.0 0.0 0.4 0.8 1.0 0.0 0.0 0.0 0.0 0.0
#> ASV_162 1.0 0.0 0.0 0.0 0.0 0.2 0.2 0.2 0.0 0.6
#> ASV_142 0.0 0.6 0.6 0.0 0.4 0.8 0.6 0.4 0.0 0.4
#> ASV_121 0.6 0.0 0.0 0.0 0.0 0.0 0.4 0.4 0.6 0.4
#> ASV_126 0.6 0.2 0.4 0.6 0.6 0.6 0.4 0.6 0.0 0.4
#> ASV_78 0.0 0.6 0.0 0.0 0.2 0.0 0.6 0.0 1.0 0.0
#> ASV_19 0.4 0.6 0.4 0.6 0.8 0.2 0.2 0.2 0.0 0.0
#> ASV_84 0.6 0.8 0.8 0.8 0.6 0.0 0.0 0.0 0.2 0.2
#> ASV_105 ASV_71 ASV_48 ASV_90 ASV_108 ASV_100 ASV_80 ASV_127 ASV_20
#> ASV_102 0.0 0.4 0.4 0.8 0.8 0.2 0.2 0.2 0.2
#> ASV_136 0.4 0.2 0.4 0.4 0.0 0.0 0.0 0.6 0.0
#> ASV_75 0.0 0.4 0.2 1.0 0.0 0.0 0.2 0.4 0.2
#> ASV_110 0.4 0.0 0.8 0.0 0.8 0.4 0.4 0.2 0.6
#> ASV_3 0.2 0.8 0.8 0.8 0.0 0.8 0.6 0.8 0.8
#> ASV_55 0.4 0.8 0.6 0.4 0.8 0.8 0.8 0.8 0.6
#> ASV_116 0.0 0.0 0.6 0.2 0.4 0.4 0.2 0.6 0.0
#> ASV_40 0.2 0.0 0.8 0.2 0.2 0.4 0.2 0.4 0.0
#> ASV_4 0.4 0.0 1.0 0.4 0.4 0.2 0.0 0.6 0.0
#> ASV_144 0.8 0.2 0.2 0.0 0.0 0.0 0.0 0.0 0.2
#> ASV_11 0.4 0.0 0.2 0.2 0.6 0.4 0.2 0.4 1.0
#> ASV_61 0.4 0.0 0.2 0.2 0.6 0.4 1.0 0.8 0.6
#> ASV_94 0.0 0.0 0.4 0.0 0.8 0.0 0.0 0.4 0.2
#> ASV_57 0.4 0.4 0.6 0.0 1.0 0.0 0.0 0.6 0.0
#> ASV_129 0.0 0.0 0.0 0.0 0.4 0.4 0.8 1.0 0.6
#> ASV_93 0.0 0.0 0.0 0.0 0.4 0.6 0.8 0.6 0.4
#> ASV_7 0.8 1.0 0.8 0.0 0.6 0.4 0.6 0.2 0.6
#> ASV_13 0.2 0.4 0.0 0.0 0.8 0.6 0.0 0.0 0.8
#> ASV_12 0.6 1.0 0.0 0.0 0.0 0.4 0.8 0.2 0.6
#> ASV_105 1.0 0.0 0.2 0.0 0.6 0.0 0.0 0.2 0.4
#> ASV_71 0.0 1.0 0.8 0.0 0.0 0.0 0.0 0.4 0.4
#> ASV_48 0.2 0.8 1.0 0.0 0.0 0.2 0.0 0.0 0.4
#> ASV_90 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.2 0.4
#> ASV_108 0.6 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0
#> ASV_100 0.0 0.0 0.2 0.0 0.0 1.0 0.0 0.4 0.8
#> ASV_80 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.4 0.6
#> ASV_127 0.2 0.4 0.0 0.2 0.0 0.4 0.4 1.0 0.0
#> ASV_20 0.4 0.4 0.4 0.4 0.0 0.8 0.6 0.0 1.0
#> ASV_69 0.4 0.6 0.0 0.0 0.4 0.6 0.6 0.6 0.6
#> ASV_28 0.8 0.8 0.2 0.8 0.0 0.6 1.0 0.0 0.0
#> ASV_92 0.0 0.0 0.0 0.0 0.0 0.0 0.6 0.0 1.0
#> ASV_150 0.4 0.0 0.0 0.8 0.4 0.8 1.0 0.4 1.0
#> ASV_72 0.2 0.2 0.8 0.6 1.0 0.6 0.6 0.2 0.4
#> ASV_76 0.6 0.8 0.4 0.2 0.2 0.6 0.0 0.4 0.2
#> ASV_30 0.8 0.6 0.6 0.6 0.4 1.0 0.4 0.6 0.2
#> ASV_83 0.4 0.8 1.0 0.2 0.2 1.0 0.2 0.6 0.6
#> ASV_50 0.8 0.6 0.4 0.2 0.8 1.0 0.2 0.2 0.4
#> ASV_99 0.0 0.2 0.4 0.0 0.4 0.4 0.2 0.6 0.2
#> ASV_36 0.2 0.2 0.4 0.2 0.2 0.8 0.4 0.8 0.4
#> ASV_38 0.2 0.2 0.4 0.0 0.2 0.6 0.2 0.6 0.2
#> ASV_35 0.0 0.8 0.4 0.6 0.6 0.4 0.4 0.8 0.8
#> ASV_81 0.4 0.6 0.6 0.6 0.4 0.6 0.4 0.4 0.2
#> ASV_114 0.0 0.0 1.0 0.6 0.8 0.0 0.4 0.4 0.8
#> ASV_14 0.8 0.6 0.4 0.0 0.6 0.8 0.8 0.0 0.2
#> ASV_166 0.6 0.6 0.8 0.6 0.6 0.6 0.8 0.6 0.6
#> ASV_123 0.8 0.2 0.0 0.4 0.2 0.6 0.8 0.2 0.0
#> ASV_104 0.6 0.0 0.2 0.4 0.2 0.4 0.4 0.4 0.0
#> ASV_85 0.8 0.6 0.4 0.6 0.2 0.2 0.4 0.4 0.0
#> ASV_98 0.4 0.2 0.0 0.6 0.4 0.8 1.0 1.0 0.4
#> ASV_134 0.8 0.0 0.0 0.2 0.8 0.4 0.0 0.0 0.0
#> ASV_64 0.4 0.0 1.0 0.2 0.8 0.6 0.0 0.0 0.2
#> ASV_141 0.0 0.0 0.4 0.8 1.0 0.2 0.2 0.6 0.2
#> ASV_56 0.0 0.0 0.8 0.2 0.8 0.2 0.4 0.2 0.4
#> ASV_60 0.2 0.2 1.0 0.8 0.4 0.4 1.0 0.0 0.0
#> ASV_2 0.4 0.2 0.6 0.8 0.8 0.2 0.6 0.2 0.2
#> ASV_6 0.0 0.0 0.6 0.8 0.8 0.6 0.8 0.0 0.2
#> ASV_39 0.0 0.0 0.8 1.0 1.0 0.4 0.8 0.0 0.4
#> ASV_44 0.6 0.4 0.8 0.6 0.6 0.6 1.0 0.4 0.2
#> ASV_10 0.2 0.0 0.0 0.2 0.0 0.4 0.8 0.2 0.2
#> ASV_62 0.0 0.0 0.0 0.0 0.2 0.2 0.8 0.2 0.6
#> ASV_96 0.0 0.2 0.4 0.2 0.0 0.4 0.6 0.6 1.0
#> ASV_24 0.4 0.2 0.4 0.2 0.2 0.8 0.0 0.4 1.0
#> ASV_47 0.4 0.4 1.0 0.6 0.2 0.2 0.4 0.0 0.4
#> ASV_63 0.2 0.0 0.6 0.2 0.8 0.2 0.4 0.0 0.0
#> ASV_79 0.0 0.2 0.6 0.4 0.6 0.2 0.2 0.0 0.2
#> ASV_17 0.0 0.4 0.2 0.8 1.0 0.2 0.6 0.0 0.2
#> ASV_41 0.4 0.4 0.6 0.2 1.0 0.6 0.2 0.0 0.2
#> ASV_73 0.0 0.6 0.6 0.0 0.8 0.6 0.4 0.0 0.2
#> ASV_120 0.6 0.4 1.0 0.2 0.2 0.6 0.0 0.0 0.8
#> ASV_137 0.0 0.8 0.4 0.2 0.0 0.8 0.2 0.2 0.8
#> ASV_130 0.4 0.0 0.2 0.8 0.4 0.4 0.0 0.4 0.4
#> ASV_107 0.2 1.0 0.6 1.0 0.4 0.8 0.6 0.6 0.4
#> ASV_115 0.2 0.6 0.4 0.4 0.4 0.6 0.2 0.8 0.8
#> ASV_118 0.2 0.2 0.4 0.6 0.0 0.4 0.6 0.0 0.2
#> ASV_70 0.8 0.2 0.4 1.0 0.2 0.6 1.0 0.2 0.0
#> ASV_103 0.8 0.4 0.2 0.2 0.2 1.0 1.0 0.0 0.0
#> ASV_133 0.2 0.0 0.4 0.6 0.6 0.0 0.8 0.4 0.4
#> ASV_23 0.6 0.0 0.4 0.0 0.0 0.8 0.2 0.0 0.2
#> ASV_42 1.0 0.6 0.2 0.0 0.2 1.0 0.4 0.0 0.4
#> ASV_45 0.4 0.4 0.4 0.0 0.0 0.4 0.4 0.0 1.0
#> ASV_140 0.0 0.0 0.8 0.0 0.2 0.2 0.8 0.2 0.4
#> ASV_97 0.4 0.0 0.2 0.8 0.2 0.6 0.8 0.4 0.2
#> ASV_29 0.0 0.0 0.4 0.0 0.8 0.6 0.8 0.4 0.4
#> ASV_147 0.4 0.6 0.2 0.6 0.4 0.0 0.0 0.0 0.2
#> ASV_54 0.0 0.0 0.6 0.2 0.4 0.4 0.8 0.8 0.2
#> ASV_15 0.0 0.0 0.0 1.0 0.6 0.2 1.0 0.8 0.2
#> ASV_156 0.0 0.0 0.4 0.2 0.4 0.4 0.2 0.2 0.4
#> ASV_132 0.2 0.4 0.4 0.6 0.2 0.0 0.0 0.4 0.6
#> ASV_22 0.0 0.2 0.2 0.0 0.8 0.2 0.6 0.2 0.0
#> ASV_68 0.0 0.2 0.8 0.0 0.2 0.0 0.4 0.4 0.0
#> ASV_59 0.6 0.2 0.4 0.2 0.2 0.0 0.6 0.6 0.2
#> ASV_87 0.6 0.0 0.0 0.0 0.8 0.8 0.6 0.8 0.6
#> ASV_74 0.0 0.0 0.0 0.0 0.6 0.2 0.0 0.6 0.2
#> ASV_65 0.4 1.0 0.6 0.0 0.8 0.8 0.8 0.8 0.4
#> ASV_66 0.0 0.0 1.0 0.2 0.2 0.0 0.8 0.2 0.6
#> ASV_101 0.0 0.0 0.8 0.6 0.2 0.8 0.2 0.6 0.8
#> ASV_122 0.0 0.0 0.6 0.2 0.0 0.0 0.0 0.2 0.4
#> ASV_163 0.0 0.0 1.0 0.2 0.6 0.0 0.2 0.0 0.4
#> ASV_32 0.2 0.0 0.6 0.6 0.2 0.6 0.8 0.0 0.2
#> ASV_128 0.0 0.0 0.8 0.4 0.4 0.0 0.4 0.0 0.2
#> ASV_33 0.2 0.2 0.8 0.4 0.2 0.4 0.8 0.8 0.0
#> ASV_77 0.2 0.0 1.0 0.6 0.4 0.4 0.6 0.0 0.0
#> ASV_159 0.0 0.2 0.2 0.2 0.2 0.4 0.8 1.0 0.8
#> ASV_46 0.0 0.0 0.2 0.6 0.6 0.4 1.0 1.0 0.4
#> ASV_112 0.0 0.2 1.0 0.4 0.8 0.4 0.4 0.0 0.2
#> ASV_89 0.4 0.2 0.8 0.8 0.8 0.4 0.8 0.2 0.2
#> ASV_111 0.4 1.0 1.0 0.2 0.8 0.6 0.8 0.8 0.2
#> ASV_37 0.8 0.4 0.0 0.0 0.6 0.8 0.2 0.6 0.8
#> ASV_1 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.2
#> ASV_86 1.0 0.6 0.4 0.2 0.0 0.0 1.0 0.6 0.0
#> ASV_18 0.0 0.8 0.4 0.6 0.6 0.4 0.6 0.0 0.0
#> ASV_34 0.0 0.4 0.0 0.8 0.0 0.0 0.4 0.4 0.2
#> ASV_25 0.4 0.8 0.8 0.6 0.0 1.0 0.8 0.0 0.6
#> ASV_124 0.2 0.8 0.2 0.0 0.4 0.6 0.8 0.8 0.2
#> ASV_119 0.0 0.6 0.0 0.6 0.6 0.2 0.2 0.4 0.4
#> ASV_153 0.0 0.2 1.0 0.4 0.4 0.2 0.4 0.0 0.0
#> ASV_52 0.0 0.0 0.0 0.4 0.0 0.0 0.0 0.4 0.2
#> ASV_21 0.2 0.8 0.6 0.0 0.0 0.0 0.6 0.0 0.0
#> ASV_49 0.2 0.8 0.6 0.2 0.0 0.0 0.0 1.0 0.8
#> ASV_53 0.4 0.6 0.4 0.4 0.0 0.0 0.0 0.4 0.8
#> ASV_138 0.0 0.0 0.2 0.2 0.0 0.0 0.0 0.0 1.0
#> ASV_16 0.0 0.2 1.0 0.6 0.8 0.0 0.2 0.4 0.2
#> ASV_95 0.4 0.2 0.0 0.8 0.0 0.0 0.0 0.2 0.2
#> ASV_9 0.0 0.0 0.6 0.6 0.0 0.0 0.0 0.6 0.0
#> ASV_135 0.0 0.2 0.2 0.2 0.0 0.0 0.2 1.0 0.8
#> ASV_31 0.0 0.0 0.4 0.8 0.0 0.0 0.6 0.0 0.2
#> ASV_91 0.2 0.0 0.2 0.2 0.2 0.2 0.2 0.8 0.4
#> ASV_67 0.2 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.4
#> ASV_43 0.4 0.0 0.4 1.0 0.4 0.6 0.2 0.2 0.2
#> ASV_172 0.4 0.0 0.4 0.4 0.2 0.8 0.2 0.0 0.4
#> ASV_51 0.2 0.6 0.4 0.8 0.0 0.2 0.2 0.2 0.8
#> ASV_117 0.2 0.2 0.6 0.6 0.0 0.6 0.0 0.0 0.2
#> ASV_8 0.0 0.0 0.2 0.0 0.8 0.2 0.0 0.2 0.2
#> ASV_106 0.0 0.2 0.8 0.0 0.4 0.4 0.6 0.0 0.4
#> ASV_125 0.4 0.0 0.0 0.8 0.0 0.6 0.0 1.0 0.6
#> ASV_113 0.6 0.0 0.6 1.0 0.0 0.0 0.0 0.6 0.4
#> ASV_109 0.0 0.0 0.0 0.0 0.0 0.8 0.8 0.4 0.4
#> ASV_26 0.0 0.0 0.0 0.6 0.2 1.0 0.6 0.0 0.0
#> ASV_154 0.2 0.8 0.0 0.0 0.2 0.8 0.0 0.0 0.8
#> ASV_88 0.0 0.0 0.0 0.0 0.6 0.4 0.8 0.4 0.2
#> ASV_151 0.0 0.0 0.0 0.0 0.4 0.6 0.6 0.4 0.4
#> ASV_27 0.2 0.0 0.2 0.2 0.0 0.6 0.0 0.4 0.2
#> ASV_148 0.0 0.0 0.2 0.4 0.0 0.4 0.8 0.4 0.2
#> ASV_162 0.2 0.0 0.8 0.8 0.8 0.4 0.4 0.0 0.0
#> ASV_142 0.4 0.0 0.8 0.2 0.4 0.2 0.4 0.0 0.4
#> ASV_121 0.2 0.2 0.2 0.8 0.8 0.2 0.4 1.0 0.6
#> ASV_126 0.8 0.2 0.6 0.0 0.8 0.6 0.8 0.0 0.0
#> ASV_78 0.0 0.0 0.6 0.2 0.8 0.0 0.2 0.4 0.6
#> ASV_19 1.0 0.6 0.4 0.6 0.2 0.6 0.2 0.6 0.0
#> ASV_84 0.2 0.0 0.0 0.0 0.2 0.8 0.8 0.6 0.4
#> ASV_69 ASV_28 ASV_92 ASV_150 ASV_72 ASV_76 ASV_30 ASV_83 ASV_50 ASV_99
#> ASV_102 0.2 0.4 0.6 0.2 0.6 0.4 0.2 0.6 1.0 0.0
#> ASV_136 0.2 0.6 0.8 0.2 0.0 0.4 0.4 0.8 0.6 0.4
#> ASV_75 0.6 1.0 0.6 0.2 0.4 0.4 0.2 0.8 0.4 0.4
#> ASV_110 1.0 1.0 0.8 1.0 0.4 0.0 0.2 0.8 0.0 0.8
#> ASV_3 0.0 0.8 0.6 0.0 0.2 0.2 0.0 0.0 0.0 0.0
#> ASV_55 0.0 0.6 0.8 0.0 0.2 0.2 0.2 0.8 0.6 0.0
#> ASV_116 0.2 0.8 0.4 0.0 0.0 0.0 1.0 0.4 0.2 0.2
#> ASV_40 0.0 0.0 0.8 0.8 0.4 0.0 0.4 0.4 0.6 0.6
#> ASV_4 0.6 0.2 0.0 0.8 0.4 1.0 0.4 0.2 0.0 0.4
#> ASV_144 0.2 0.8 0.4 0.0 0.2 0.0 0.0 0.4 1.0 0.0
#> ASV_11 0.6 0.6 0.8 0.6 0.8 0.4 0.0 0.2 0.2 0.2
#> ASV_61 0.8 1.0 0.6 0.2 0.0 0.0 0.0 1.0 0.4 0.4
#> ASV_94 0.0 0.6 0.2 0.4 0.6 1.0 0.8 0.8 1.0 0.0
#> ASV_57 0.4 0.2 0.2 0.2 0.0 0.6 0.2 0.6 0.8 0.6
#> ASV_129 0.8 1.0 0.0 0.8 0.2 0.6 0.6 0.8 0.2 0.4
#> ASV_93 0.4 1.0 0.8 0.2 0.8 0.8 0.2 0.8 0.8 0.6
#> ASV_7 0.0 0.4 0.0 0.6 0.0 0.4 0.0 0.2 0.2 0.4
#> ASV_13 0.8 0.4 0.2 0.4 0.0 0.0 0.2 0.8 0.8 0.8
#> ASV_12 0.0 1.0 0.8 0.0 0.2 0.8 0.0 0.0 0.0 0.0
#> ASV_105 0.4 0.8 0.0 0.4 0.2 0.6 0.8 0.4 0.8 0.0
#> ASV_71 0.6 0.8 0.0 0.0 0.2 0.8 0.6 0.8 0.6 0.2
#> ASV_48 0.0 0.2 0.0 0.0 0.8 0.4 0.6 1.0 0.4 0.4
#> ASV_90 0.0 0.8 0.0 0.8 0.6 0.2 0.6 0.2 0.2 0.0
#> ASV_108 0.4 0.0 0.0 0.4 1.0 0.2 0.4 0.2 0.8 0.4
#> ASV_100 0.6 0.6 0.0 0.8 0.6 0.6 1.0 1.0 1.0 0.4
#> ASV_80 0.6 1.0 0.6 1.0 0.6 0.0 0.4 0.2 0.2 0.2
#> ASV_127 0.6 0.0 0.0 0.4 0.2 0.4 0.6 0.6 0.2 0.6
#> ASV_20 0.6 0.0 1.0 1.0 0.4 0.2 0.2 0.6 0.4 0.2
#> ASV_69 1.0 0.0 0.0 0.0 0.6 0.8 0.0 1.0 0.8 0.0
#> ASV_28 0.0 1.0 0.0 0.8 0.2 0.0 0.2 0.6 0.8 0.4
#> ASV_92 0.0 0.0 1.0 0.6 0.0 0.2 0.0 0.0 0.6 0.0
#> ASV_150 0.0 0.8 0.6 1.0 0.0 0.0 0.8 0.8 0.2 0.2
#> ASV_72 0.6 0.2 0.0 0.0 1.0 0.0 0.8 1.0 0.6 0.8
#> ASV_76 0.8 0.0 0.2 0.0 0.0 1.0 0.2 0.2 0.6 0.8
#> ASV_30 0.0 0.2 0.0 0.8 0.8 0.2 1.0 1.0 0.0 0.0
#> ASV_83 1.0 0.6 0.0 0.8 1.0 0.2 1.0 1.0 0.0 1.0
#> ASV_50 0.8 0.8 0.6 0.2 0.6 0.6 0.0 0.0 1.0 0.0
#> ASV_99 0.0 0.4 0.0 0.2 0.8 0.8 0.0 1.0 0.0 1.0
#> ASV_36 0.2 0.4 0.6 0.8 1.0 0.2 0.0 1.0 0.0 0.0
#> ASV_38 0.2 0.8 0.2 0.6 0.6 0.2 0.0 0.0 0.0 0.0
#> ASV_35 0.8 0.8 1.0 0.2 0.0 0.0 0.4 0.4 0.4 0.2
#> ASV_81 0.8 0.6 0.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_114 1.0 0.8 0.2 1.0 0.2 0.4 0.6 0.2 0.0 0.6
#> ASV_14 0.2 0.8 0.8 0.0 0.2 0.0 0.0 0.0 0.0 0.0
#> ASV_166 0.0 0.2 0.0 0.0 0.4 0.0 0.8 0.0 0.0 0.6
#> ASV_123 0.0 1.0 0.6 0.2 0.4 0.2 0.8 0.0 0.0 0.6
#> ASV_104 0.0 0.2 0.2 0.4 0.6 0.8 0.8 0.0 0.0 0.8
#> ASV_85 0.0 0.4 0.8 0.2 0.6 0.4 1.0 0.0 0.0 0.6
#> ASV_98 1.0 0.6 0.0 0.6 0.2 0.8 0.6 0.0 0.0 0.4
#> ASV_134 0.6 0.8 0.8 1.0 0.6 0.2 0.4 0.0 0.0 0.6
#> ASV_64 0.8 1.0 0.6 0.0 0.8 0.0 0.2 0.0 0.0 0.2
#> ASV_141 0.8 0.8 0.2 0.8 0.2 0.4 0.2 0.0 0.0 0.4
#> ASV_56 1.0 0.4 0.2 0.0 0.0 0.4 0.2 0.0 0.0 0.6
#> ASV_60 1.0 0.4 0.2 0.8 0.2 0.6 0.6 0.0 0.0 0.2
#> ASV_2 0.8 0.4 0.2 0.8 0.2 0.4 0.0 0.0 0.0 0.0
#> ASV_6 0.6 0.2 0.2 0.4 0.4 0.6 0.2 0.0 0.0 0.6
#> ASV_39 1.0 0.4 0.2 0.6 0.6 0.0 0.0 0.0 0.0 0.6
#> ASV_44 0.4 0.6 0.2 0.8 0.4 1.0 0.0 0.0 0.0 0.2
#> ASV_10 0.6 0.6 0.4 0.2 0.4 0.8 0.2 0.0 0.0 0.4
#> ASV_62 1.0 0.4 0.2 1.0 0.6 0.4 0.6 0.0 0.0 0.6
#> ASV_96 0.8 0.2 0.2 0.4 0.6 0.2 0.4 0.0 0.0 1.0
#> ASV_24 0.8 0.0 0.6 0.8 0.8 0.0 0.2 0.0 0.0 0.2
#> ASV_47 0.8 0.0 0.6 0.8 0.6 0.0 0.4 0.0 0.0 0.6
#> ASV_63 0.8 0.6 0.2 0.6 0.0 0.0 0.8 0.0 0.0 0.6
#> ASV_79 0.2 0.0 0.2 0.0 0.6 0.4 0.2 0.0 0.0 0.6
#> ASV_17 0.8 1.0 0.2 0.0 0.4 0.0 0.0 0.0 0.0 0.2
#> ASV_41 0.8 0.8 0.4 1.0 0.8 0.0 0.4 0.0 0.0 0.2
#> ASV_73 0.8 1.0 0.4 0.4 0.8 0.2 0.4 0.0 0.0 0.4
#> ASV_120 0.8 0.4 1.0 0.2 0.8 0.0 0.4 0.4 0.2 1.0
#> ASV_137 0.8 0.8 0.8 1.0 0.8 0.2 0.2 0.0 0.0 0.2
#> ASV_130 0.4 0.8 0.0 0.2 0.6 0.2 0.2 0.6 0.8 1.0
#> ASV_107 0.2 0.0 0.6 0.2 0.2 0.2 0.0 0.4 0.6 0.4
#> ASV_115 0.8 0.8 0.2 0.4 0.8 0.8 0.0 0.6 0.2 0.0
#> ASV_118 0.8 0.8 0.2 0.8 1.0 1.0 0.6 0.0 0.8 0.4
#> ASV_70 1.0 0.0 1.0 0.0 0.6 0.2 0.0 0.0 0.2 0.6
#> ASV_103 0.0 0.4 0.4 0.6 0.8 0.2 0.6 0.0 0.2 0.0
#> ASV_133 0.6 1.0 0.6 0.8 0.6 1.0 0.8 0.0 0.0 0.2
#> ASV_23 0.0 1.0 0.8 0.0 0.4 0.2 1.0 0.8 0.4 0.8
#> ASV_42 0.6 0.4 0.6 0.4 0.4 0.0 0.0 0.2 0.0 0.2
#> ASV_45 0.2 0.8 0.8 0.0 0.8 0.0 0.0 0.2 0.2 0.2
#> ASV_140 0.0 1.0 0.2 0.8 0.8 0.6 1.0 0.6 0.0 0.2
#> ASV_97 0.4 0.4 0.0 0.6 0.4 0.4 0.0 0.0 0.0 0.2
#> ASV_29 0.4 1.0 0.0 0.6 0.2 0.8 1.0 0.2 0.0 0.8
#> ASV_147 0.0 0.4 0.8 0.4 0.4 0.8 0.4 0.8 1.0 0.0
#> ASV_54 0.2 1.0 0.0 0.0 0.0 0.0 0.2 0.2 0.2 0.0
#> ASV_15 0.0 0.2 0.2 0.2 0.8 0.0 1.0 0.8 0.2 0.0
#> ASV_156 0.0 0.0 0.2 0.8 0.8 0.8 0.4 1.0 0.8 0.0
#> ASV_132 0.8 0.6 0.8 0.2 0.0 0.6 0.0 0.6 0.2 0.2
#> ASV_22 0.6 0.0 0.4 0.6 0.6 0.8 0.2 0.8 1.0 0.0
#> ASV_68 0.0 0.0 0.4 0.8 0.6 0.2 0.2 0.2 0.2 0.0
#> ASV_59 0.4 1.0 0.8 0.0 0.2 0.6 0.0 0.6 0.2 0.4
#> ASV_87 0.4 0.4 0.8 0.8 0.0 0.8 0.0 0.6 0.8 0.6
#> ASV_74 0.0 0.0 0.2 0.6 0.8 0.6 0.0 0.6 0.8 0.0
#> ASV_65 0.2 0.8 0.2 0.8 0.8 0.4 0.8 0.6 0.8 0.4
#> ASV_66 0.8 1.0 0.2 0.0 0.2 0.6 0.0 0.0 0.0 0.4
#> ASV_101 0.8 1.0 1.0 0.4 0.8 0.2 0.0 0.2 0.0 0.2
#> ASV_122 0.4 0.8 0.2 0.0 0.8 0.4 0.2 0.0 0.0 0.8
#> ASV_163 0.4 1.0 0.2 0.0 1.0 0.6 0.0 0.0 0.0 0.0
#> ASV_32 0.8 0.8 0.8 0.4 0.6 0.6 0.2 0.2 0.0 0.2
#> ASV_128 0.8 0.6 0.2 0.6 0.2 0.6 0.0 0.2 0.0 0.8
#> ASV_33 0.8 0.4 0.4 0.2 0.0 0.6 0.0 0.0 0.0 0.6
#> ASV_77 0.8 0.2 0.6 0.2 0.0 0.4 0.2 0.2 0.0 0.2
#> ASV_159 1.0 0.0 0.2 0.4 0.0 0.6 0.8 0.4 0.2 0.8
#> ASV_46 0.2 0.2 0.0 0.0 0.0 0.4 0.8 0.4 0.4 0.4
#> ASV_112 0.8 0.4 0.2 0.8 0.0 0.6 0.2 0.4 0.2 0.4
#> ASV_89 0.6 0.4 0.4 0.8 0.0 0.4 0.0 0.0 0.0 0.4
#> ASV_111 0.4 0.6 0.6 0.2 0.0 0.6 0.2 0.0 0.0 0.2
#> ASV_37 0.0 1.0 0.8 0.0 0.0 1.0 0.4 0.4 0.0 0.6
#> ASV_1 0.4 0.0 0.2 0.0 0.8 0.8 0.4 0.4 0.6 0.8
#> ASV_86 1.0 0.0 0.2 0.4 0.2 0.4 0.0 0.4 0.2 0.2
#> ASV_18 0.4 0.0 0.2 0.2 0.6 0.2 0.8 0.6 0.6 0.2
#> ASV_34 0.2 0.0 0.0 0.6 0.4 1.0 0.2 0.0 0.4 0.4
#> ASV_25 0.4 0.6 0.8 0.2 0.4 0.4 0.0 0.0 0.2 0.0
#> ASV_124 0.6 0.4 0.6 0.2 0.0 0.2 0.8 0.4 1.0 0.8
#> ASV_119 0.6 0.6 0.4 0.6 0.0 0.4 0.0 0.0 0.0 0.2
#> ASV_153 0.6 0.2 0.4 0.4 0.0 0.0 0.0 0.0 0.4 0.8
#> ASV_52 0.2 0.8 0.2 0.6 0.6 0.4 0.2 0.0 0.4 0.8
#> ASV_21 0.4 0.0 0.4 0.0 0.2 0.4 0.0 0.2 0.0 0.2
#> ASV_49 0.4 0.6 0.8 0.2 0.4 0.4 0.0 0.4 0.0 0.0
#> ASV_53 0.6 0.4 0.0 0.6 0.6 1.0 0.0 0.0 0.0 0.2
#> ASV_138 0.4 0.6 0.4 0.8 0.2 0.2 0.2 0.4 0.6 0.2
#> ASV_16 0.4 0.4 0.8 0.2 0.6 1.0 0.4 0.8 0.4 0.0
#> ASV_95 0.4 0.2 0.6 0.8 0.6 0.2 0.6 0.8 0.6 0.8
#> ASV_9 0.8 0.0 0.2 0.2 0.2 0.0 0.0 0.2 0.6 0.2
#> ASV_135 0.0 0.2 0.2 0.0 0.8 0.0 0.0 0.0 0.0 0.8
#> ASV_31 0.0 0.0 0.0 0.0 0.4 0.0 1.0 0.2 0.6 0.0
#> ASV_91 0.0 0.6 0.2 0.0 0.2 0.2 0.8 0.8 0.2 0.2
#> ASV_67 0.6 0.4 0.2 0.0 0.2 0.6 1.0 0.2 0.0 0.4
#> ASV_43 0.6 0.6 0.6 0.0 1.0 0.6 0.8 0.0 0.0 0.8
#> ASV_172 0.8 0.6 0.8 0.0 1.0 0.6 0.8 0.0 0.0 0.6
#> ASV_51 0.0 0.0 0.0 0.4 0.2 0.6 0.2 0.2 0.4 0.4
#> ASV_117 0.4 0.4 0.8 0.2 0.6 0.4 0.0 0.4 1.0 0.0
#> ASV_8 0.4 0.8 0.0 1.0 0.2 0.0 0.4 0.6 0.6 0.0
#> ASV_106 0.8 0.8 0.8 0.2 0.4 0.8 0.0 1.0 0.2 0.0
#> ASV_125 0.4 0.4 0.0 0.0 1.0 0.2 0.4 0.0 0.0 0.4
#> ASV_113 0.8 0.0 0.0 0.8 0.0 0.4 0.2 0.2 0.2 0.2
#> ASV_109 0.0 1.0 0.4 0.2 0.6 0.4 0.4 0.0 0.0 0.6
#> ASV_26 0.0 0.6 0.6 0.0 0.0 0.4 0.6 0.0 0.4 0.6
#> ASV_154 0.0 0.2 0.0 0.8 1.0 0.4 0.0 0.4 0.8 0.2
#> ASV_88 0.0 1.0 0.2 0.2 0.6 0.4 0.4 0.8 0.6 0.6
#> ASV_151 0.0 1.0 0.6 0.0 1.0 0.8 0.6 1.0 0.4 0.6
#> ASV_27 0.0 0.2 1.0 0.2 0.4 0.2 0.0 0.0 0.0 0.0
#> ASV_148 0.0 0.6 0.6 0.4 0.0 0.8 0.0 0.0 0.0 0.0
#> ASV_162 0.4 0.4 0.8 0.8 0.4 0.4 0.4 0.2 0.2 0.2
#> ASV_142 0.8 0.6 0.0 0.4 0.6 0.2 0.0 0.0 0.0 0.0
#> ASV_121 0.8 0.6 0.8 0.4 0.8 0.4 0.0 0.2 0.0 0.6
#> ASV_126 1.0 0.8 0.4 0.6 1.0 0.4 0.2 0.0 0.0 0.0
#> ASV_78 0.8 0.8 0.4 0.2 0.4 0.4 0.0 0.0 0.0 0.0
#> ASV_19 1.0 0.0 0.6 0.4 0.2 0.0 0.0 0.0 0.0 0.0
#> ASV_84 0.8 0.4 0.4 0.2 1.0 0.4 0.2 0.4 0.4 1.0
#> ASV_36 ASV_38 ASV_35 ASV_81 ASV_114 ASV_14 ASV_166 ASV_123 ASV_104
#> ASV_102 0.6 0.0 1.0 0.6 0.2 0.2 0.2 0.2 0.4
#> ASV_136 0.2 0.2 0.4 0.0 0.6 0.0 0.6 0.4 0.2
#> ASV_75 0.8 0.4 0.6 0.8 0.8 0.4 0.6 0.6 0.6
#> ASV_110 0.4 0.4 0.0 0.0 0.0 0.0 0.0 0.4 0.4
#> ASV_3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.6 0.0
#> ASV_55 0.4 0.2 0.0 0.0 0.2 0.0 0.0 0.2 0.0
#> ASV_116 0.8 0.4 1.0 1.0 0.2 0.6 0.0 0.2 0.2
#> ASV_40 0.4 0.8 0.6 0.0 0.6 0.6 1.0 0.0 0.8
#> ASV_4 0.2 0.4 1.0 0.4 0.0 0.6 0.0 0.4 0.8
#> ASV_144 0.0 0.0 0.0 0.2 0.2 0.6 1.0 0.2 0.8
#> ASV_11 0.4 0.4 0.0 0.2 0.4 0.0 0.4 0.2 0.6
#> ASV_61 1.0 0.8 0.8 0.6 0.2 0.0 0.4 1.0 0.2
#> ASV_94 0.6 0.4 0.4 0.4 0.0 0.8 0.0 0.8 0.6
#> ASV_57 0.8 0.2 0.8 0.8 0.0 0.0 0.0 0.2 0.6
#> ASV_129 0.4 0.4 0.0 0.8 0.0 0.8 0.6 0.6 0.4
#> ASV_93 1.0 0.4 0.0 0.4 0.0 0.8 0.0 0.0 0.4
#> ASV_7 0.2 0.2 0.4 0.2 0.2 1.0 0.2 0.2 0.8
#> ASV_13 0.2 0.2 0.2 0.2 0.8 0.8 0.0 0.0 0.8
#> ASV_12 0.4 0.4 0.6 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_105 0.2 0.2 0.0 0.4 0.0 0.8 0.6 0.8 0.6
#> ASV_71 0.2 0.2 0.8 0.6 0.0 0.6 0.6 0.2 0.0
#> ASV_48 0.4 0.4 0.4 0.6 1.0 0.4 0.8 0.0 0.2
#> ASV_90 0.2 0.0 0.6 0.6 0.6 0.0 0.6 0.4 0.4
#> ASV_108 0.2 0.2 0.6 0.4 0.8 0.6 0.6 0.2 0.2
#> ASV_100 0.8 0.6 0.4 0.6 0.0 0.8 0.6 0.6 0.4
#> ASV_80 0.4 0.2 0.4 0.4 0.4 0.8 0.8 0.8 0.4
#> ASV_127 0.8 0.6 0.8 0.4 0.4 0.0 0.6 0.2 0.4
#> ASV_20 0.4 0.2 0.8 0.2 0.8 0.2 0.6 0.0 0.0
#> ASV_69 0.2 0.2 0.8 0.8 1.0 0.2 0.0 0.0 0.0
#> ASV_28 0.4 0.8 0.8 0.6 0.8 0.8 0.2 1.0 0.2
#> ASV_92 0.6 0.2 1.0 0.6 0.2 0.8 0.0 0.6 0.2
#> ASV_150 0.8 0.6 0.2 0.0 1.0 0.0 0.0 0.2 0.4
#> ASV_72 1.0 0.6 0.0 0.0 0.2 0.2 0.4 0.4 0.6
#> ASV_76 0.2 0.2 0.0 0.0 0.4 0.0 0.0 0.2 0.8
#> ASV_30 0.0 0.0 0.4 0.0 0.6 0.0 0.8 0.8 0.8
#> ASV_83 1.0 0.0 0.4 0.0 0.2 0.0 0.0 0.0 0.0
#> ASV_50 0.0 0.0 0.4 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_99 0.0 0.0 0.2 0.0 0.6 0.0 0.6 0.6 0.8
#> ASV_36 1.0 0.0 0.4 0.0 0.2 0.0 0.2 0.0 0.0
#> ASV_38 0.0 1.0 0.2 0.0 1.0 0.2 0.0 0.4 0.0
#> ASV_35 0.4 0.2 1.0 0.0 0.0 0.2 0.0 0.4 0.2
#> ASV_81 0.0 0.0 0.0 1.0 0.2 0.0 0.0 0.8 0.2
#> ASV_114 0.2 1.0 0.0 0.2 1.0 0.0 0.0 0.0 0.0
#> ASV_14 0.0 0.2 0.2 0.0 0.0 1.0 0.0 0.4 0.0
#> ASV_166 0.2 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.4
#> ASV_123 0.0 0.4 0.4 0.8 0.0 0.4 0.0 1.0 0.0
#> ASV_104 0.0 0.0 0.2 0.2 0.0 0.0 0.4 0.0 1.0
#> ASV_85 0.6 0.6 0.8 0.4 1.0 0.8 0.6 0.0 0.0
#> ASV_98 0.0 0.2 1.0 0.4 0.6 0.6 0.6 0.0 0.0
#> ASV_134 0.0 0.0 0.0 0.2 0.0 0.2 0.0 0.0 0.0
#> ASV_64 0.0 0.0 0.4 0.2 0.0 0.0 0.0 0.0 0.0
#> ASV_141 0.0 0.2 0.8 0.8 0.0 0.4 0.8 0.0 0.0
#> ASV_56 0.8 0.8 0.2 0.6 0.0 0.4 0.8 0.0 0.0
#> ASV_60 0.0 0.0 0.4 0.2 0.0 0.0 0.0 0.0 0.0
#> ASV_2 0.0 0.0 0.4 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_6 0.0 0.0 0.4 0.8 0.0 0.0 0.2 0.0 0.0
#> ASV_39 0.0 0.0 1.0 0.0 0.0 0.0 0.2 0.0 0.0
#> ASV_44 0.0 0.0 0.4 0.2 0.0 0.2 0.4 0.0 0.0
#> ASV_10 0.0 0.0 0.4 0.2 0.0 0.0 0.4 0.0 0.0
#> ASV_62 0.0 0.0 0.4 0.2 0.0 0.2 0.2 0.0 0.0
#> ASV_96 0.2 0.2 1.0 0.2 0.0 0.2 0.4 0.0 0.2
#> ASV_24 0.0 0.0 0.4 0.0 0.0 0.0 0.2 0.0 0.0
#> ASV_47 0.0 0.2 0.6 0.6 0.0 0.0 0.4 0.0 0.0
#> ASV_63 0.0 0.6 0.4 0.4 0.0 0.0 0.8 0.0 0.0
#> ASV_79 0.6 0.2 0.2 0.2 0.2 0.0 0.6 0.0 0.0
#> ASV_17 0.0 0.0 0.4 0.2 0.0 0.0 0.2 0.0 0.0
#> ASV_41 0.0 0.0 0.4 1.0 0.0 0.0 0.0 0.0 0.0
#> ASV_73 0.0 0.0 0.4 0.0 0.0 0.0 0.8 0.0 0.0
#> ASV_120 0.6 0.8 0.4 1.0 0.2 0.2 0.4 0.0 0.0
#> ASV_137 0.8 0.8 1.0 0.4 0.0 0.2 0.0 0.0 0.0
#> ASV_130 0.6 0.4 0.6 0.8 0.8 1.0 0.4 0.2 0.0
#> ASV_107 0.8 0.8 0.8 0.2 0.6 0.6 0.4 1.0 0.0
#> ASV_115 0.0 0.0 0.0 0.2 0.8 0.0 0.2 0.0 0.0
#> ASV_118 0.0 0.0 0.0 0.6 0.0 0.0 0.0 0.2 0.0
#> ASV_70 0.0 0.0 0.6 0.0 1.0 0.8 0.8 0.0 0.0
#> ASV_103 0.2 0.0 0.2 1.0 0.6 0.0 0.2 0.0 0.2
#> ASV_133 0.0 0.0 0.4 0.4 0.6 0.8 0.2 0.0 0.0
#> ASV_23 0.4 0.4 0.8 0.4 0.2 0.0 0.0 0.2 0.8
#> ASV_42 0.4 0.4 0.6 0.6 0.0 0.0 0.4 0.8 1.0
#> ASV_45 0.0 0.2 0.4 0.8 0.0 0.0 0.0 0.4 0.0
#> ASV_140 0.8 0.8 0.6 0.6 0.0 0.0 0.2 0.0 0.4
#> ASV_97 0.0 0.2 0.4 0.0 0.0 0.0 0.0 0.2 0.2
#> ASV_29 0.4 0.6 0.0 0.4 0.0 0.0 0.0 1.0 0.4
#> ASV_147 0.8 0.6 0.4 0.0 0.4 0.6 0.4 0.0 0.4
#> ASV_54 0.2 0.2 0.0 0.0 0.2 0.0 0.0 1.0 0.2
#> ASV_15 0.0 0.0 0.2 0.0 0.6 0.6 0.8 0.2 0.2
#> ASV_156 0.4 0.4 0.4 0.0 1.0 1.0 0.6 0.0 0.0
#> ASV_132 0.6 0.4 0.0 0.2 0.0 0.6 0.2 0.2 0.4
#> ASV_22 0.0 0.0 0.8 0.6 0.6 0.2 0.8 1.0 0.4
#> ASV_68 0.2 0.0 0.8 0.4 1.0 0.8 0.6 1.0 0.8
#> ASV_59 0.4 0.6 0.2 0.4 0.6 0.6 0.6 1.0 0.8
#> ASV_87 0.8 0.6 0.6 0.2 0.0 0.0 0.4 0.4 0.2
#> ASV_74 0.2 0.2 0.0 0.0 0.2 0.2 0.4 0.0 0.4
#> ASV_65 0.4 0.6 0.4 0.2 0.8 1.0 0.4 0.0 0.8
#> ASV_66 0.0 0.0 0.4 0.2 0.0 0.0 0.0 0.0 0.0
#> ASV_101 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.4 0.6
#> ASV_122 0.2 0.2 0.2 0.4 0.0 0.0 0.0 0.0 0.0
#> ASV_163 0.0 0.0 0.4 0.2 0.0 0.0 0.0 0.0 0.2
#> ASV_32 0.4 0.6 0.8 0.6 0.0 0.4 0.2 0.4 0.0
#> ASV_128 0.0 0.4 0.0 1.0 0.0 0.0 0.4 0.2 0.2
#> ASV_33 0.0 0.0 0.4 0.2 0.0 0.4 0.2 0.2 0.4
#> ASV_77 0.4 0.0 0.0 0.2 0.2 0.6 0.4 0.2 0.0
#> ASV_159 0.8 0.6 0.8 0.8 0.0 0.4 1.0 0.2 0.0
#> ASV_46 0.8 0.6 0.2 0.6 0.2 0.8 0.8 0.0 0.0
#> ASV_112 1.0 0.4 0.2 0.0 0.0 0.8 1.0 0.0 0.0
#> ASV_89 0.2 0.6 0.6 0.0 0.0 0.0 0.4 0.0 0.0
#> ASV_111 0.2 0.4 0.4 0.2 0.2 0.6 0.6 0.0 0.0
#> ASV_37 0.6 0.8 0.2 0.6 0.8 0.2 0.0 0.2 0.0
#> ASV_1 0.4 0.6 0.8 0.4 0.6 1.0 0.6 0.2 0.0
#> ASV_86 0.2 0.2 0.8 0.0 0.4 0.4 1.0 0.6 0.0
#> ASV_18 0.2 0.2 0.4 0.8 0.6 0.4 1.0 1.0 0.6
#> ASV_34 0.4 0.6 0.6 0.8 0.2 0.8 0.8 0.0 0.0
#> ASV_25 0.0 0.0 0.4 0.2 0.6 0.4 0.6 0.0 0.2
#> ASV_124 0.6 0.0 0.0 0.6 1.0 0.2 0.4 0.8 0.4
#> ASV_119 0.2 0.8 0.2 0.4 0.2 0.8 0.2 0.0 0.0
#> ASV_153 0.0 0.4 0.8 0.6 0.4 1.0 0.0 0.0 0.4
#> ASV_52 0.4 0.6 0.4 0.8 0.8 0.6 0.4 0.2 0.0
#> ASV_21 0.0 0.2 0.4 0.0 0.4 0.8 0.8 0.2 0.4
#> ASV_49 0.0 0.0 0.6 0.0 0.6 0.6 0.2 0.2 0.2
#> ASV_53 0.0 0.0 0.6 0.2 0.4 0.4 0.8 0.0 0.0
#> ASV_138 0.4 0.6 0.0 0.2 0.8 0.2 0.2 0.0 0.4
#> ASV_16 0.2 0.2 0.0 0.0 0.8 0.6 0.2 0.0 0.4
#> ASV_95 0.4 0.8 0.0 0.4 0.6 1.0 0.2 0.0 0.8
#> ASV_9 0.4 0.6 0.6 1.0 0.0 0.0 0.4 0.6 0.2
#> ASV_135 0.8 0.8 0.2 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_31 0.4 0.4 0.6 0.6 0.0 0.2 0.4 0.0 0.2
#> ASV_91 0.8 0.4 0.0 0.2 0.0 0.2 0.0 0.4 0.2
#> ASV_67 0.4 0.6 0.2 0.6 0.0 0.0 0.0 0.0 0.6
#> ASV_43 0.6 0.6 0.0 0.6 0.0 0.0 0.0 0.0 0.0
#> ASV_172 0.6 0.6 0.0 0.2 0.0 0.0 0.0 0.0 0.2
#> ASV_51 0.2 0.4 0.6 0.2 0.6 0.4 0.4 0.0 0.6
#> ASV_117 0.2 0.6 0.8 0.0 0.0 0.2 1.0 0.0 0.2
#> ASV_8 0.2 0.0 1.0 0.0 0.0 0.8 0.6 0.0 0.0
#> ASV_106 0.2 0.2 0.8 0.0 0.0 0.8 0.8 0.0 0.0
#> ASV_125 0.0 0.2 0.6 0.2 0.2 0.6 0.4 0.0 0.0
#> ASV_113 0.2 0.2 0.4 0.2 0.0 0.0 0.2 0.4 0.8
#> ASV_109 0.0 0.0 0.6 0.2 1.0 0.8 0.0 0.0 0.0
#> ASV_26 0.0 0.0 0.8 0.6 0.4 0.0 0.0 0.2 0.0
#> ASV_154 0.2 0.6 0.0 0.2 0.2 0.6 0.2 0.0 1.0
#> ASV_88 0.4 0.2 0.0 0.2 0.0 0.2 0.0 0.8 0.0
#> ASV_151 1.0 0.6 0.0 0.2 0.0 0.0 0.0 0.6 0.0
#> ASV_27 0.0 0.0 0.0 0.0 0.6 0.8 0.6 1.0 0.4
#> ASV_148 0.0 0.0 0.8 0.0 0.4 0.0 0.2 0.6 0.0
#> ASV_162 0.2 0.2 0.4 0.0 0.2 0.0 0.0 1.0 0.6
#> ASV_142 0.0 0.0 0.6 0.0 0.2 0.0 0.0 0.0 0.0
#> ASV_121 0.2 0.4 0.8 0.6 0.0 0.0 0.0 0.4 0.4
#> ASV_126 0.0 0.0 0.6 1.0 0.0 0.0 0.8 0.0 0.0
#> ASV_78 0.0 0.0 1.0 0.0 0.2 0.2 0.0 0.2 0.0
#> ASV_19 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.4 0.8
#> ASV_84 0.4 0.0 0.2 0.2 0.0 0.4 0.6 0.0 0.4
#> ASV_85 ASV_98 ASV_134 ASV_64 ASV_141 ASV_56 ASV_60 ASV_2 ASV_6 ASV_39
#> ASV_102 1.0 0.8 0.8 0.8 0.8 0.4 0.2 0.8 0.4 0.8
#> ASV_136 0.0 0.0 0.8 0.8 0.2 1.0 0.8 0.6 0.8 0.4
#> ASV_75 0.6 1.0 0.4 0.6 0.0 0.0 0.2 0.0 1.0 1.0
#> ASV_110 0.2 0.8 0.8 0.0 0.6 0.6 0.4 0.4 0.8 1.0
#> ASV_3 0.8 0.8 0.0 0.0 0.6 0.0 0.6 0.6 1.0 0.2
#> ASV_55 0.2 0.0 0.4 0.0 0.0 0.0 0.0 0.2 0.0 0.4
#> ASV_116 1.0 0.2 0.2 0.4 0.6 0.2 0.2 0.2 0.4 0.4
#> ASV_40 0.4 0.6 0.2 0.4 0.4 0.8 0.2 0.8 0.4 0.2
#> ASV_4 1.0 0.0 0.6 0.2 0.0 0.0 0.0 0.0 0.2 0.0
#> ASV_144 0.2 0.4 0.2 0.6 0.4 0.8 0.8 0.8 1.0 0.6
#> ASV_11 0.0 0.2 0.0 1.0 0.4 0.2 0.2 0.8 0.8 0.0
#> ASV_61 0.0 0.4 0.0 0.4 0.8 0.6 0.6 0.4 0.8 0.4
#> ASV_94 0.6 0.8 0.2 0.8 0.8 0.2 0.4 0.6 0.4 0.2
#> ASV_57 0.0 0.6 0.0 0.4 1.0 0.2 0.6 0.8 0.2 1.0
#> ASV_129 0.6 0.0 0.0 0.0 0.4 0.0 0.2 0.6 0.4 0.6
#> ASV_93 0.6 0.2 0.2 0.0 0.0 0.8 1.0 0.8 0.4 0.4
#> ASV_7 1.0 0.4 0.8 0.4 0.4 1.0 0.6 0.6 0.8 0.6
#> ASV_13 0.6 0.2 0.4 0.2 0.6 0.8 0.6 0.6 0.8 0.6
#> ASV_12 1.0 0.8 0.6 0.8 0.2 0.0 0.0 0.2 0.0 0.0
#> ASV_105 0.8 0.4 0.8 0.4 0.0 0.0 0.2 0.4 0.0 0.0
#> ASV_71 0.6 0.2 0.0 0.0 0.0 0.0 0.2 0.2 0.0 0.0
#> ASV_48 0.4 0.0 0.0 1.0 0.4 0.8 1.0 0.6 0.6 0.8
#> ASV_90 0.6 0.6 0.2 0.2 0.8 0.2 0.8 0.8 0.8 1.0
#> ASV_108 0.2 0.4 0.8 0.8 1.0 0.8 0.4 0.8 0.8 1.0
#> ASV_100 0.2 0.8 0.4 0.6 0.2 0.2 0.4 0.2 0.6 0.4
#> ASV_80 0.4 1.0 0.0 0.0 0.2 0.4 1.0 0.6 0.8 0.8
#> ASV_127 0.4 1.0 0.0 0.0 0.6 0.2 0.0 0.2 0.0 0.0
#> ASV_20 0.0 0.4 0.0 0.2 0.2 0.4 0.0 0.2 0.2 0.4
#> ASV_69 0.0 1.0 0.6 0.8 0.8 1.0 1.0 0.8 0.6 1.0
#> ASV_28 0.4 0.6 0.8 1.0 0.8 0.4 0.4 0.4 0.2 0.4
#> ASV_92 0.8 0.0 0.8 0.6 0.2 0.2 0.2 0.2 0.2 0.2
#> ASV_150 0.2 0.6 1.0 0.0 0.8 0.0 0.8 0.8 0.4 0.6
#> ASV_72 0.6 0.2 0.6 0.8 0.2 0.0 0.2 0.2 0.4 0.6
#> ASV_76 0.4 0.8 0.2 0.0 0.4 0.4 0.6 0.4 0.6 0.0
#> ASV_30 1.0 0.6 0.4 0.2 0.2 0.2 0.6 0.0 0.2 0.0
#> ASV_83 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_50 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_99 0.6 0.4 0.6 0.2 0.4 0.6 0.2 0.0 0.6 0.6
#> ASV_36 0.6 0.0 0.0 0.0 0.0 0.8 0.0 0.0 0.0 0.0
#> ASV_38 0.6 0.2 0.0 0.0 0.2 0.8 0.0 0.0 0.0 0.0
#> ASV_35 0.8 1.0 0.0 0.4 0.8 0.2 0.4 0.4 0.4 1.0
#> ASV_81 0.4 0.4 0.2 0.2 0.8 0.6 0.2 0.0 0.8 0.0
#> ASV_114 1.0 0.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_14 0.8 0.6 0.2 0.0 0.4 0.4 0.0 0.0 0.0 0.0
#> ASV_166 0.6 0.6 0.0 0.0 0.8 0.8 0.0 0.0 0.2 0.2
#> ASV_123 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_104 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_85 1.0 0.0 0.0 0.0 0.2 0.2 0.0 0.0 0.0 0.2
#> ASV_98 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_134 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_64 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_141 0.2 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0
#> ASV_56 0.2 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0
#> ASV_60 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0
#> ASV_2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0
#> ASV_6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0
#> ASV_39 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0
#> ASV_44 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_10 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_62 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_96 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_24 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_47 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_63 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_79 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_17 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_41 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_73 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_120 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_137 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_130 0.8 0.6 0.4 0.8 1.0 0.4 1.0 0.8 0.6 0.4
#> ASV_107 0.0 0.0 0.0 0.6 1.0 0.0 0.0 0.8 0.0 0.0
#> ASV_115 0.8 0.8 0.8 0.6 0.4 0.0 0.0 0.6 0.2 0.2
#> ASV_118 0.2 0.0 0.0 0.8 0.4 0.4 1.0 0.0 0.6 0.0
#> ASV_70 0.4 0.4 0.2 0.6 0.4 0.4 0.0 0.0 0.0 0.4
#> ASV_103 0.0 0.2 0.4 0.4 0.6 0.8 0.4 0.6 0.4 0.4
#> ASV_133 0.0 0.2 0.4 1.0 0.2 0.6 0.0 0.2 0.0 0.2
#> ASV_23 0.4 0.8 0.0 0.2 0.0 0.4 0.0 0.4 0.0 0.0
#> ASV_42 0.6 0.2 0.8 0.8 0.2 0.6 0.2 1.0 0.0 0.6
#> ASV_45 1.0 1.0 0.4 0.4 0.8 0.8 0.0 0.6 0.0 0.0
#> ASV_140 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_97 0.2 0.0 0.0 0.0 0.2 0.0 0.0 0.0 0.0 0.2
#> ASV_29 0.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_147 0.0 0.2 0.0 0.0 0.6 0.2 0.0 0.0 0.6 0.2
#> ASV_54 0.2 0.0 0.6 1.0 0.2 0.0 0.8 0.6 0.4 0.4
#> ASV_15 0.2 0.0 0.2 0.6 0.6 0.6 0.4 0.6 0.2 0.6
#> ASV_156 0.2 0.0 0.0 1.0 0.6 0.0 0.2 0.4 0.0 0.2
#> ASV_132 0.4 0.2 0.4 0.0 0.8 0.6 0.0 0.4 1.0 0.4
#> ASV_22 0.6 0.2 1.0 0.6 0.8 0.0 0.2 0.8 0.6 0.4
#> ASV_68 1.0 0.6 0.2 0.6 0.8 0.8 0.2 0.2 0.8 0.2
#> ASV_59 0.0 0.2 0.6 0.0 0.2 0.0 0.6 0.4 0.0 0.4
#> ASV_87 0.0 0.0 0.0 0.0 0.2 0.2 0.4 0.2 0.2 0.2
#> ASV_74 0.6 0.0 0.0 0.0 0.8 0.0 0.2 1.0 0.2 0.2
#> ASV_65 0.8 1.0 0.8 0.6 0.6 0.4 0.8 0.4 0.6 0.8
#> ASV_66 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_101 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_122 0.2 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_163 0.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_32 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_128 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_33 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_77 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_159 0.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_46 0.0 0.0 0.0 0.6 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_112 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_89 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_111 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_37 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_1 0.6 0.0 0.2 0.4 0.2 0.0 0.2 0.0 0.2 0.2
#> ASV_86 1.0 0.6 0.6 0.6 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_18 0.8 0.8 0.6 0.4 0.4 0.0 0.2 0.6 0.4 0.4
#> ASV_34 0.6 0.0 0.6 0.6 0.0 0.0 0.2 0.2 0.0 0.0
#> ASV_25 0.0 0.0 0.4 0.4 0.4 0.6 0.0 0.2 0.4 0.2
#> ASV_124 0.0 0.4 0.6 0.6 0.8 0.4 0.8 0.4 0.6 0.4
#> ASV_119 0.0 0.0 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_153 0.0 0.2 0.4 0.6 0.2 0.4 0.2 0.2 0.4 0.4
#> ASV_52 0.8 0.0 0.6 0.6 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_21 0.8 0.2 0.8 0.8 0.6 0.0 0.0 0.0 0.0 0.2
#> ASV_49 0.0 0.6 0.8 0.2 0.2 0.0 0.4 0.0 0.4 0.2
#> ASV_53 0.6 0.0 0.2 0.2 0.4 0.0 0.0 0.0 0.2 0.0
#> ASV_138 0.2 0.2 0.0 1.0 1.0 0.6 0.8 0.8 0.8 0.8
#> ASV_16 0.4 0.4 0.2 0.8 1.0 0.0 0.6 0.6 0.4 0.6
#> ASV_95 0.2 0.2 0.0 0.2 0.8 0.6 1.0 0.6 1.0 0.4
#> ASV_9 0.0 0.6 0.4 0.6 0.4 0.0 0.2 0.2 0.2 0.2
#> ASV_135 0.4 0.2 0.0 0.0 0.2 0.2 0.2 0.2 0.2 0.0
#> ASV_31 0.8 1.0 0.4 0.8 0.2 0.2 0.2 0.4 0.2 0.2
#> ASV_91 1.0 1.0 0.6 0.8 0.2 0.4 0.2 0.6 0.2 0.8
#> ASV_67 0.4 0.8 0.6 0.0 0.0 0.2 0.2 0.4 0.0 0.2
#> ASV_43 0.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_172 0.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_51 0.0 0.8 0.8 0.8 0.0 0.8 0.6 0.2 0.6 0.8
#> ASV_117 0.0 0.0 0.0 0.0 0.6 0.2 0.0 0.4 0.8 0.2
#> ASV_8 0.4 0.0 0.4 0.2 0.0 0.0 0.0 0.2 0.0 0.0
#> ASV_106 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_125 0.2 0.0 0.0 0.2 0.4 0.2 0.0 0.0 0.0 0.0
#> ASV_113 0.2 0.0 0.2 0.2 0.6 0.4 0.4 0.0 0.0 0.0
#> ASV_109 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_26 0.2 0.0 0.0 0.0 0.2 0.6 0.0 0.0 0.6 0.0
#> ASV_154 0.2 0.0 0.0 0.0 0.8 0.4 0.8 0.0 0.6 0.2
#> ASV_88 0.8 0.4 0.0 0.0 1.0 0.2 0.4 0.0 0.8 0.8
#> ASV_151 0.2 0.0 0.0 0.0 0.4 0.4 0.2 0.8 0.8 0.8
#> ASV_27 0.4 0.2 0.4 0.2 0.2 0.0 0.2 0.0 0.4 0.0
#> ASV_148 0.4 0.0 0.0 0.0 0.2 0.4 0.4 0.0 0.2 0.0
#> ASV_162 0.6 0.6 1.0 0.2 1.0 0.4 0.0 0.2 0.2 0.0
#> ASV_142 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_121 0.8 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_126 0.6 0.0 0.0 0.0 0.2 0.2 0.0 0.0 0.0 0.0
#> ASV_78 0.4 0.4 0.2 1.0 0.0 0.2 1.0 0.0 0.0 0.8
#> ASV_19 0.2 0.2 0.0 0.0 1.0 1.0 0.0 0.0 0.2 0.0
#> ASV_84 0.8 0.4 0.0 0.0 1.0 0.4 0.4 0.4 0.6 0.2
#> ASV_44 ASV_10 ASV_62 ASV_96 ASV_24 ASV_47 ASV_63 ASV_79 ASV_17 ASV_41
#> ASV_102 0.6 0.6 0.6 0.4 0.8 0.2 0.8 0.4 0.8 0.8
#> ASV_136 0.6 0.8 0.4 0.6 0.4 0.4 1.0 0.2 0.6 1.0
#> ASV_75 0.0 0.2 0.6 0.2 0.4 0.8 0.0 0.8 0.6 0.8
#> ASV_110 0.2 0.2 0.4 0.4 0.4 0.8 0.8 0.8 0.6 0.8
#> ASV_3 0.0 0.0 0.2 0.6 0.4 0.6 0.8 0.8 0.6 0.6
#> ASV_55 0.2 0.2 0.8 0.4 0.0 0.4 0.0 0.0 0.4 0.0
#> ASV_116 0.2 0.2 0.6 0.8 0.6 0.8 0.4 1.0 0.6 0.4
#> ASV_40 0.6 0.4 0.8 0.2 0.0 0.4 0.6 0.0 0.0 0.4
#> ASV_4 0.0 0.0 0.2 0.2 0.4 0.8 0.2 0.4 0.0 0.2
#> ASV_144 0.6 1.0 1.0 0.8 1.0 0.2 0.0 0.2 0.6 0.6
#> ASV_11 0.6 0.6 0.6 0.8 0.8 0.8 0.8 0.8 0.4 0.4
#> ASV_61 0.0 0.6 0.8 1.0 0.8 0.6 0.6 0.6 0.6 0.4
#> ASV_94 0.4 0.8 0.4 0.4 0.8 0.8 1.0 0.6 0.8 0.8
#> ASV_57 0.2 0.6 0.8 0.8 0.6 1.0 0.4 0.8 0.8 0.6
#> ASV_129 0.2 0.0 0.0 0.0 0.6 0.6 0.6 0.6 0.8 0.4
#> ASV_93 0.4 0.4 0.2 0.8 0.8 0.6 0.2 0.0 0.0 0.6
#> ASV_7 0.0 0.4 0.2 0.2 0.8 0.6 0.6 0.2 0.8 0.6
#> ASV_13 0.4 0.2 0.2 0.6 0.6 0.4 0.0 0.0 0.0 0.4
#> ASV_12 0.2 0.8 0.4 0.4 0.2 0.0 0.0 0.0 0.0 0.0
#> ASV_105 0.6 0.2 0.0 0.0 0.4 0.4 0.2 0.0 0.0 0.4
#> ASV_71 0.4 0.0 0.0 0.2 0.2 0.4 0.0 0.2 0.4 0.4
#> ASV_48 0.8 0.0 0.0 0.4 0.4 1.0 0.6 0.6 0.2 0.6
#> ASV_90 0.6 0.2 0.0 0.2 0.2 0.6 0.2 0.4 0.8 0.2
#> ASV_108 0.6 0.0 0.2 0.0 0.2 0.2 0.8 0.6 1.0 1.0
#> ASV_100 0.6 0.4 0.2 0.4 0.8 0.2 0.2 0.2 0.2 0.6
#> ASV_80 1.0 0.8 0.8 0.6 0.0 0.4 0.4 0.2 0.6 0.2
#> ASV_127 0.4 0.2 0.2 0.6 0.4 0.0 0.0 0.0 0.0 0.0
#> ASV_20 0.2 0.2 0.6 1.0 1.0 0.4 0.0 0.2 0.2 0.2
#> ASV_69 0.4 0.6 1.0 0.8 0.8 0.8 0.8 0.2 0.8 0.8
#> ASV_28 0.6 0.6 0.4 0.2 0.0 0.0 0.6 0.0 1.0 0.8
#> ASV_92 0.2 0.4 0.2 0.2 0.6 0.6 0.2 0.2 0.2 0.4
#> ASV_150 0.8 0.2 1.0 0.4 0.8 0.8 0.6 0.0 0.0 1.0
#> ASV_72 0.4 0.4 0.6 0.6 0.8 0.6 0.0 0.6 0.4 0.8
#> ASV_76 1.0 0.8 0.4 0.2 0.0 0.0 0.0 0.4 0.0 0.0
#> ASV_30 0.0 0.2 0.6 0.4 0.2 0.4 0.8 0.2 0.0 0.4
#> ASV_83 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_50 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_99 0.2 0.4 0.6 1.0 0.2 0.6 0.6 0.6 0.2 0.2
#> ASV_36 0.0 0.0 0.0 0.2 0.0 0.0 0.0 0.6 0.0 0.0
#> ASV_38 0.0 0.0 0.0 0.2 0.0 0.2 0.6 0.2 0.0 0.0
#> ASV_35 0.4 0.4 0.4 1.0 0.4 0.6 0.4 0.2 0.4 0.4
#> ASV_81 0.2 0.2 0.2 0.2 0.0 0.6 0.4 0.2 0.2 1.0
#> ASV_114 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.0 0.0
#> ASV_14 0.2 0.0 0.2 0.2 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_166 0.4 0.4 0.2 0.4 0.2 0.4 0.8 0.6 0.2 0.0
#> ASV_123 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_104 0.0 0.0 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_85 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_98 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_134 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_141 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_56 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_60 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_39 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_44 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_10 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_62 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_96 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_24 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0
#> ASV_47 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0
#> ASV_63 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0
#> ASV_79 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0
#> ASV_17 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0
#> ASV_41 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0
#> ASV_73 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_120 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_137 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_130 0.6 0.4 0.4 0.4 0.6 0.8 1.0 0.8 0.4 0.6
#> ASV_107 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_115 0.6 0.0 0.6 0.4 0.2 0.8 0.4 0.0 0.2 0.0
#> ASV_118 0.0 0.0 0.2 0.4 0.4 0.4 0.6 0.2 0.0 0.0
#> ASV_70 0.4 0.4 0.8 0.6 0.0 0.0 0.4 0.6 0.0 0.0
#> ASV_103 0.6 0.4 0.8 0.8 0.8 0.8 0.2 0.6 0.2 0.2
#> ASV_133 0.0 0.6 0.6 0.8 0.6 0.6 0.4 0.2 0.0 0.0
#> ASV_23 0.8 0.6 0.0 0.2 0.2 0.2 0.0 0.0 0.0 0.2
#> ASV_42 0.6 1.0 0.6 0.2 0.0 1.0 0.0 0.0 0.8 1.0
#> ASV_45 0.8 0.8 0.4 0.4 0.0 0.4 0.0 0.0 0.0 0.6
#> ASV_140 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_97 0.0 0.0 0.0 0.0 0.2 0.0 0.0 0.0 0.0 0.0
#> ASV_29 0.0 0.0 0.0 0.0 0.4 0.0 0.0 0.4 0.4 0.0
#> ASV_147 0.2 0.4 0.2 0.8 1.0 0.2 0.0 0.0 0.4 0.0
#> ASV_54 0.6 0.4 0.2 0.0 0.8 0.6 0.0 0.2 0.6 0.2
#> ASV_15 0.6 0.4 0.0 0.2 0.6 0.4 0.2 0.0 0.6 0.6
#> ASV_156 0.8 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.6 0.2
#> ASV_132 0.0 0.4 0.2 0.0 0.2 0.4 1.0 0.6 1.0 0.8
#> ASV_22 0.4 1.0 0.0 0.0 0.0 0.8 0.2 0.2 0.6 0.6
#> ASV_68 0.4 0.6 0.2 0.6 0.4 0.4 0.2 0.2 0.2 0.2
#> ASV_59 0.0 0.0 0.0 0.0 0.4 1.0 0.6 0.6 0.4 0.6
#> ASV_87 0.2 0.0 0.2 0.0 0.0 0.8 0.4 0.2 0.4 0.2
#> ASV_74 0.0 0.0 0.2 0.0 0.2 0.6 0.0 0.6 0.6 0.0
#> ASV_65 0.6 0.0 0.6 1.0 0.6 0.2 0.6 0.6 1.0 0.4
#> ASV_66 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_101 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.0 0.0
#> ASV_122 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_163 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_32 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.0
#> ASV_128 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_33 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_77 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.0
#> ASV_159 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.0
#> ASV_46 0.0 0.0 0.0 0.2 0.2 0.0 0.0 0.0 0.0 0.0
#> ASV_112 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_89 0.0 0.0 0.2 0.0 0.0 0.2 0.0 0.0 0.0 0.0
#> ASV_111 0.0 0.2 0.4 0.0 0.2 0.0 0.0 0.0 0.0 0.0
#> ASV_37 0.0 0.0 0.2 0.0 0.2 0.0 0.0 0.0 0.0 0.0
#> ASV_1 0.2 0.0 0.0 0.0 0.0 0.4 0.0 0.0 0.2 0.0
#> ASV_86 0.2 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_18 0.8 0.6 0.4 0.0 0.0 0.0 0.0 0.0 1.0 0.8
#> ASV_34 0.4 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_25 0.4 0.2 0.4 0.4 0.4 0.4 0.2 0.4 0.2 0.2
#> ASV_124 0.6 0.4 0.4 0.6 0.6 0.6 1.0 0.6 0.8 0.8
#> ASV_119 0.2 0.2 0.2 0.2 0.6 0.0 0.0 0.0 0.0 0.0
#> ASV_153 0.4 0.4 0.6 0.6 0.8 0.2 0.4 0.2 0.2 0.2
#> ASV_52 0.4 0.4 0.2 0.2 0.4 0.0 0.0 0.0 0.0 0.0
#> ASV_21 0.2 0.2 0.2 0.0 0.0 0.0 0.0 0.2 0.0 0.0
#> ASV_49 0.0 0.0 0.4 0.2 0.4 0.8 0.2 0.4 0.2 0.4
#> ASV_53 0.0 0.0 0.0 0.6 0.2 0.8 0.0 0.2 0.0 0.0
#> ASV_138 0.2 0.8 0.4 0.4 0.4 0.8 0.4 0.2 0.8 0.8
#> ASV_16 0.6 0.6 0.8 0.4 0.4 0.4 0.2 0.2 0.6 0.2
#> ASV_95 0.2 0.8 0.2 0.4 0.2 0.4 0.8 0.2 0.8 0.4
#> ASV_9 0.6 0.2 0.2 0.0 0.0 0.0 0.0 0.0 0.2 0.2
#> ASV_135 0.2 0.2 0.0 0.0 0.0 0.0 0.2 0.0 0.2 0.0
#> ASV_31 0.4 0.2 0.2 0.0 0.0 0.0 0.0 0.0 0.2 0.2
#> ASV_91 0.2 0.4 0.4 1.0 0.8 0.6 0.8 0.6 0.2 0.8
#> ASV_67 0.2 0.0 0.8 0.4 0.0 0.2 0.0 0.0 0.0 0.0
#> ASV_43 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_172 0.0 0.0 0.0 0.0 0.0 0.2 0.0 0.0 0.0 0.0
#> ASV_51 0.6 0.8 0.4 0.8 0.8 0.6 0.6 0.8 0.2 0.6
#> ASV_117 0.4 0.0 0.0 0.2 0.4 0.8 0.0 0.0 0.2 0.0
#> ASV_8 0.2 0.0 0.0 0.0 0.4 0.0 0.0 0.0 0.0 0.0
#> ASV_106 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_125 0.0 0.0 0.2 0.6 1.0 0.0 0.2 0.0 0.0 0.0
#> ASV_113 0.4 0.0 0.0 0.0 0.6 0.0 0.0 0.2 0.2 0.0
#> ASV_109 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_26 0.0 0.0 0.0 0.2 0.6 0.4 0.8 0.0 0.0 0.0
#> ASV_154 0.0 0.2 0.4 0.4 0.6 0.8 0.4 0.6 0.6 0.8
#> ASV_88 0.0 0.0 0.0 0.2 0.4 0.8 1.0 0.4 0.6 0.4
#> ASV_151 0.0 0.0 0.0 0.4 0.8 0.0 0.8 0.6 0.4 0.4
#> ASV_27 0.0 0.0 0.0 0.0 0.8 0.2 0.8 0.8 0.4 0.4
#> ASV_148 0.0 0.0 0.0 0.0 0.2 0.4 0.2 0.6 0.2 0.2
#> ASV_162 0.2 0.0 0.0 0.6 0.6 0.4 0.2 0.4 0.0 0.4
#> ASV_142 0.0 0.0 0.0 0.6 0.0 0.0 0.0 0.2 0.0 0.0
#> ASV_121 0.0 0.0 0.2 0.2 0.0 0.0 0.0 0.2 0.0 0.2
#> ASV_126 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_78 0.2 1.0 1.0 0.8 0.4 0.6 0.4 0.0 0.4 0.2
#> ASV_19 0.4 0.2 0.0 0.2 0.0 0.0 0.2 0.6 0.6 0.2
#> ASV_84 0.0 0.0 0.0 0.0 0.0 0.4 0.8 0.6 1.0 0.6
#> ASV_73 ASV_120 ASV_137 ASV_130 ASV_107 ASV_115 ASV_118 ASV_70 ASV_103
#> ASV_102 0.4 0.2 0.4 0.8 0.6 0.2 0.0 0.6 1.0
#> ASV_136 0.2 1.0 0.4 0.6 0.8 0.2 1.0 1.0 1.0
#> ASV_75 0.2 0.8 1.0 0.4 0.6 0.2 0.6 1.0 1.0
#> ASV_110 0.6 1.0 0.6 0.0 0.2 0.0 0.8 0.4 0.8
#> ASV_3 0.6 0.4 0.6 0.0 0.2 0.0 0.2 0.8 0.8
#> ASV_55 0.4 0.0 0.0 0.4 0.4 0.0 1.0 0.8 0.2
#> ASV_116 0.4 0.6 0.4 0.0 0.4 1.0 0.8 0.6 0.0
#> ASV_40 0.0 0.0 0.0 0.0 0.0 0.2 1.0 0.2 0.8
#> ASV_4 0.2 0.0 0.2 0.0 0.2 0.2 0.6 0.8 0.4
#> ASV_144 0.6 0.4 0.2 0.0 0.0 0.2 0.6 0.4 0.6
#> ASV_11 0.4 0.8 0.4 0.4 0.0 0.0 0.2 0.6 1.0
#> ASV_61 0.2 0.0 0.6 1.0 0.0 0.2 0.2 0.6 1.0
#> ASV_94 0.2 0.2 0.2 0.2 1.0 0.0 0.0 0.6 0.8
#> ASV_57 0.4 0.0 0.6 0.0 0.0 0.0 0.4 0.6 0.8
#> ASV_129 0.4 0.8 0.0 0.4 0.6 0.8 0.8 0.6 1.0
#> ASV_93 0.4 0.2 0.6 0.0 0.0 0.0 0.4 0.2 0.6
#> ASV_7 0.4 0.4 1.0 0.0 0.0 0.0 0.4 0.8 0.8
#> ASV_13 0.0 0.0 0.8 0.0 0.0 0.0 0.0 0.4 0.8
#> ASV_12 0.0 0.0 0.8 0.0 0.0 0.0 0.0 0.2 0.0
#> ASV_105 0.0 0.6 0.0 0.4 0.2 0.2 0.2 0.8 0.8
#> ASV_71 0.6 0.4 0.8 0.0 1.0 0.6 0.2 0.2 0.4
#> ASV_48 0.6 1.0 0.4 0.2 0.6 0.4 0.4 0.4 0.2
#> ASV_90 0.0 0.2 0.2 0.8 1.0 0.4 0.6 1.0 0.2
#> ASV_108 0.8 0.2 0.0 0.4 0.4 0.4 0.0 0.2 0.2
#> ASV_100 0.6 0.6 0.8 0.4 0.8 0.6 0.4 0.6 1.0
#> ASV_80 0.4 0.0 0.2 0.0 0.6 0.2 0.6 1.0 1.0
#> ASV_127 0.0 0.0 0.2 0.4 0.6 0.8 0.0 0.2 0.0
#> ASV_20 0.2 0.8 0.8 0.4 0.4 0.8 0.2 0.0 0.0
#> ASV_69 0.8 0.8 0.8 0.4 0.2 0.8 0.8 1.0 0.0
#> ASV_28 1.0 0.4 0.8 0.8 0.0 0.8 0.8 0.0 0.4
#> ASV_92 0.4 1.0 0.8 0.0 0.6 0.2 0.2 1.0 0.4
#> ASV_150 0.4 0.2 1.0 0.2 0.2 0.4 0.8 0.0 0.6
#> ASV_72 0.8 0.8 0.8 0.6 0.2 0.8 1.0 0.6 0.8
#> ASV_76 0.2 0.0 0.2 0.2 0.2 0.8 1.0 0.2 0.2
#> ASV_30 0.4 0.4 0.2 0.2 0.0 0.0 0.6 0.0 0.6
#> ASV_83 0.0 0.4 0.0 0.6 0.4 0.6 0.0 0.0 0.0
#> ASV_50 0.0 0.2 0.0 0.8 0.6 0.2 0.8 0.2 0.2
#> ASV_99 0.4 1.0 0.2 1.0 0.4 0.0 0.4 0.6 0.0
#> ASV_36 0.0 0.6 0.8 0.6 0.8 0.0 0.0 0.0 0.2
#> ASV_38 0.0 0.8 0.8 0.4 0.8 0.0 0.0 0.0 0.0
#> ASV_35 0.4 0.4 1.0 0.6 0.8 0.0 0.0 0.6 0.2
#> ASV_81 0.0 1.0 0.4 0.8 0.2 0.2 0.6 0.0 1.0
#> ASV_114 0.0 0.2 0.0 0.8 0.6 0.8 0.0 1.0 0.6
#> ASV_14 0.0 0.2 0.2 1.0 0.6 0.0 0.0 0.8 0.0
#> ASV_166 0.8 0.4 0.0 0.4 0.4 0.2 0.0 0.8 0.2
#> ASV_123 0.0 0.0 0.0 0.2 1.0 0.0 0.2 0.0 0.0
#> ASV_104 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2
#> ASV_85 0.0 0.0 0.0 0.8 0.0 0.8 0.2 0.4 0.0
#> ASV_98 0.0 0.0 0.0 0.6 0.0 0.8 0.0 0.4 0.2
#> ASV_134 0.0 0.0 0.0 0.4 0.0 0.8 0.0 0.2 0.4
#> ASV_64 0.0 0.0 0.0 0.8 0.6 0.6 0.8 0.6 0.4
#> ASV_141 0.0 0.0 0.0 1.0 1.0 0.4 0.4 0.4 0.6
#> ASV_56 0.0 0.0 0.0 0.4 0.0 0.0 0.4 0.4 0.8
#> ASV_60 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.4
#> ASV_2 0.0 0.0 0.0 0.8 0.8 0.6 0.0 0.0 0.6
#> ASV_6 0.0 0.0 0.0 0.6 0.0 0.2 0.6 0.0 0.4
#> ASV_39 0.0 0.0 0.0 0.4 0.0 0.2 0.0 0.4 0.4
#> ASV_44 0.0 0.0 0.0 0.6 0.0 0.6 0.0 0.4 0.6
#> ASV_10 0.0 0.0 0.0 0.4 0.2 0.0 0.0 0.4 0.4
#> ASV_62 0.0 0.0 0.0 0.4 0.0 0.6 0.2 0.8 0.8
#> ASV_96 0.0 0.0 0.0 0.4 0.0 0.4 0.4 0.6 0.8
#> ASV_24 0.0 0.0 0.0 0.6 0.0 0.2 0.4 0.0 0.8
#> ASV_47 0.0 0.0 0.0 0.8 0.0 0.8 0.4 0.0 0.8
#> ASV_63 0.0 0.0 0.0 1.0 0.0 0.4 0.6 0.4 0.2
#> ASV_79 0.0 0.0 0.0 0.8 0.0 0.0 0.2 0.6 0.6
#> ASV_17 0.0 0.0 0.0 0.4 0.0 0.2 0.0 0.0 0.2
#> ASV_41 0.0 0.0 0.0 0.6 0.0 0.0 0.0 0.0 0.2
#> ASV_73 1.0 0.0 0.0 0.6 0.0 0.0 0.0 0.0 0.2
#> ASV_120 0.0 1.0 0.0 0.6 0.0 0.0 0.4 0.4 1.0
#> ASV_137 0.0 0.0 1.0 0.0 0.0 0.8 0.2 1.0 1.0
#> ASV_130 0.6 0.6 0.0 1.0 0.8 0.0 1.0 0.0 0.6
#> ASV_107 0.0 0.0 0.0 0.8 1.0 0.0 0.6 0.0 0.6
#> ASV_115 0.0 0.0 0.8 0.0 0.0 1.0 0.4 0.4 0.4
#> ASV_118 0.0 0.4 0.2 1.0 0.6 0.4 1.0 0.2 0.8
#> ASV_70 0.0 0.4 1.0 0.0 0.0 0.4 0.2 1.0 0.4
#> ASV_103 0.2 1.0 1.0 0.6 0.6 0.4 0.8 0.4 1.0
#> ASV_133 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.0 0.0
#> ASV_23 0.2 0.0 0.0 1.0 0.6 0.6 1.0 0.6 1.0
#> ASV_42 0.8 0.0 0.8 0.8 1.0 0.4 0.0 0.0 0.8
#> ASV_45 0.6 0.0 0.0 0.0 0.6 1.0 0.0 0.0 1.0
#> ASV_140 0.0 0.0 0.0 1.0 0.2 0.4 0.0 1.0 0.6
#> ASV_97 0.0 0.8 0.2 0.0 0.6 0.0 0.0 0.8 0.8
#> ASV_29 0.4 0.0 0.0 0.0 0.4 0.0 0.6 0.4 0.8
#> ASV_147 0.4 0.4 0.2 0.8 0.6 0.2 0.0 1.0 0.6
#> ASV_54 0.8 0.2 0.2 0.8 0.8 0.0 0.2 0.6 1.0
#> ASV_15 0.0 0.2 0.0 0.8 0.0 0.0 1.0 0.2 0.4
#> ASV_156 0.0 0.0 0.0 0.6 0.0 0.0 0.4 0.4 0.6
#> ASV_132 0.2 0.6 0.2 1.0 0.4 0.2 0.0 0.6 0.8
#> ASV_22 0.0 0.0 0.2 0.2 0.0 0.0 0.4 0.0 0.6
#> ASV_68 0.4 1.0 0.4 0.0 0.4 0.6 0.8 0.6 0.2
#> ASV_59 0.6 0.0 0.0 0.2 0.6 0.2 0.2 0.4 1.0
#> ASV_87 0.2 0.4 0.0 0.2 0.2 0.4 0.0 1.0 0.6
#> ASV_74 0.2 0.6 0.0 0.4 0.2 0.0 0.8 0.2 0.8
#> ASV_65 0.4 0.8 0.8 0.6 0.6 0.0 0.4 0.8 0.0
#> ASV_66 0.0 0.0 0.0 0.6 0.2 0.0 0.0 0.4 0.6
#> ASV_101 0.0 0.0 0.0 0.2 0.4 0.0 0.4 0.2 1.0
#> ASV_122 0.0 0.0 0.0 0.2 0.0 0.8 0.4 0.4 0.4
#> ASV_163 0.0 0.0 0.0 0.8 0.0 0.4 0.4 0.6 0.6
#> ASV_32 0.0 0.0 0.0 0.2 0.4 0.0 0.4 1.0 0.6
#> ASV_128 0.0 1.0 0.0 0.0 0.8 0.4 0.8 0.8 0.6
#> ASV_33 0.0 0.0 0.0 0.2 0.8 1.0 0.0 1.0 0.4
#> ASV_77 0.2 0.0 0.0 0.8 1.0 1.0 0.8 1.0 0.4
#> ASV_159 0.0 0.0 0.2 0.4 0.6 0.6 0.6 0.6 1.0
#> ASV_46 0.0 0.0 0.0 0.4 0.0 0.0 0.0 0.2 0.6
#> ASV_112 0.0 0.0 0.0 0.8 0.0 0.0 1.0 0.4 0.6
#> ASV_89 0.0 0.0 0.0 0.2 0.0 1.0 0.8 0.0 0.6
#> ASV_111 0.0 0.0 0.0 0.6 0.0 0.6 0.6 0.6 0.4
#> ASV_37 0.0 0.0 0.2 0.8 0.6 0.8 0.2 0.8 0.6
#> ASV_1 0.0 0.2 0.8 1.0 0.4 0.4 0.0 0.0 1.0
#> ASV_86 0.0 0.0 0.8 0.6 0.0 0.8 0.4 0.0 1.0
#> ASV_18 0.0 0.0 0.2 0.6 0.0 0.4 0.2 0.0 0.4
#> ASV_34 0.0 0.0 0.2 0.6 0.0 0.4 0.4 0.0 0.6
#> ASV_25 0.4 1.0 0.6 0.6 0.8 0.4 0.2 0.2 0.0
#> ASV_124 1.0 0.8 1.0 0.4 0.4 0.8 0.0 0.4 0.0
#> ASV_119 0.0 0.0 0.6 0.2 0.4 0.4 0.0 0.4 0.0
#> ASV_153 0.2 1.0 0.8 0.4 0.8 0.8 0.6 0.4 0.0
#> ASV_52 0.0 0.6 0.4 0.8 0.4 0.2 0.0 0.0 0.0
#> ASV_21 0.0 0.4 0.8 0.8 0.2 0.6 0.6 0.0 0.8
#> ASV_49 0.4 0.8 0.4 0.4 0.4 1.0 0.0 0.2 0.0
#> ASV_53 0.0 0.4 0.0 0.6 0.8 0.8 0.0 0.0 0.0
#> ASV_138 1.0 1.0 0.4 0.8 0.4 0.2 0.2 0.4 0.4
#> ASV_16 0.6 0.2 0.6 0.2 0.0 0.0 0.6 0.2 0.8
#> ASV_95 0.4 0.2 1.0 0.6 0.2 0.4 0.2 0.2 0.8
#> ASV_9 0.2 0.0 0.2 1.0 0.0 0.4 0.8 0.0 0.8
#> ASV_135 0.0 0.0 0.0 0.6 0.0 0.4 0.8 0.6 0.4
#> ASV_31 0.2 0.2 0.2 0.6 0.0 0.4 0.8 0.4 0.6
#> ASV_91 0.8 0.8 0.4 0.6 0.6 0.6 0.0 0.8 0.6
#> ASV_67 0.0 0.0 0.0 0.2 0.4 0.6 0.0 0.8 0.2
#> ASV_43 0.0 0.4 0.0 0.0 0.6 0.2 0.0 0.2 0.6
#> ASV_172 0.0 0.0 0.0 0.4 1.0 0.2 0.0 0.2 0.8
#> ASV_51 0.8 1.0 0.6 0.2 0.4 0.0 0.0 0.4 0.0
#> ASV_117 0.0 0.6 0.4 0.2 0.0 0.0 0.2 0.6 0.0
#> ASV_8 0.0 0.0 0.0 0.6 0.0 0.0 0.8 0.8 0.2
#> ASV_106 0.0 0.2 0.0 0.8 0.2 0.0 0.8 0.8 0.8
#> ASV_125 0.0 0.6 0.0 0.0 0.2 0.2 0.0 0.0 0.0
#> ASV_113 0.0 0.6 0.2 0.0 0.6 0.2 0.2 0.8 0.0
#> ASV_109 0.0 0.0 0.0 0.6 0.4 0.2 0.0 0.6 0.4
#> ASV_26 0.4 0.2 0.8 0.4 0.6 0.6 0.0 0.2 0.2
#> ASV_154 0.2 0.8 0.6 1.0 0.6 0.2 0.2 0.2 0.8
#> ASV_88 0.8 0.6 0.0 0.2 0.6 0.0 1.0 0.6 0.6
#> ASV_151 1.0 0.6 0.0 0.0 0.4 0.2 0.2 0.8 1.0
#> ASV_27 0.6 0.4 0.6 0.0 0.0 0.0 0.4 0.8 0.0
#> ASV_148 0.8 0.4 0.6 0.0 0.0 0.0 0.2 0.6 0.4
#> ASV_162 1.0 0.6 0.6 0.0 0.0 0.0 0.0 0.4 0.2
#> ASV_142 0.2 0.4 0.0 0.0 0.8 0.0 0.8 0.8 0.2
#> ASV_121 0.0 0.0 0.0 0.2 0.6 0.0 0.0 0.8 0.0
#> ASV_126 0.0 0.6 0.4 0.2 0.6 1.0 0.0 0.8 0.0
#> ASV_78 0.8 0.8 0.0 0.0 0.0 0.2 0.8 0.6 0.6
#> ASV_19 0.2 0.8 0.2 0.0 0.0 0.2 0.4 0.4 0.8
#> ASV_84 0.2 0.8 0.2 0.2 0.8 0.0 0.6 1.0 0.8
#> ASV_133 ASV_23 ASV_42 ASV_45 ASV_140 ASV_97 ASV_29 ASV_147 ASV_54
#> ASV_102 0.2 0.8 0.0 0.0 0.2 0.8 0.4 0.0 0.8
#> ASV_136 0.2 0.6 0.6 0.8 0.4 0.6 0.6 0.8 0.0
#> ASV_75 0.4 0.6 1.0 0.2 0.6 0.6 0.6 0.8 0.8
#> ASV_110 0.4 0.4 0.6 1.0 0.0 0.4 0.0 0.0 0.6
#> ASV_3 0.6 0.8 0.4 0.8 0.2 0.2 0.0 0.2 0.0
#> ASV_55 0.2 0.6 1.0 1.0 0.6 0.6 0.0 0.0 0.0
#> ASV_116 0.8 1.0 1.0 0.8 0.6 0.2 0.0 0.8 0.0
#> ASV_40 0.0 0.4 0.6 0.4 0.8 0.6 0.2 0.6 0.0
#> ASV_4 0.4 0.2 0.0 1.0 0.0 0.0 0.0 0.6 0.8
#> ASV_144 0.8 0.0 0.0 0.2 0.6 0.4 0.8 0.0 0.6
#> ASV_11 0.0 0.8 0.2 0.4 0.0 0.2 0.0 0.4 0.4
#> ASV_61 0.2 0.8 0.2 0.4 0.2 0.0 0.2 0.2 0.6
#> ASV_94 0.2 0.6 0.4 0.4 0.2 0.2 0.0 0.0 0.8
#> ASV_57 0.0 0.4 0.4 0.0 0.6 0.0 0.0 0.0 0.8
#> ASV_129 0.0 1.0 0.8 0.8 0.0 0.0 0.0 0.4 0.8
#> ASV_93 0.0 0.2 0.4 1.0 0.4 0.4 0.0 0.6 0.4
#> ASV_7 0.8 0.4 1.0 0.8 1.0 0.0 0.0 0.0 0.4
#> ASV_13 0.0 0.2 0.2 0.2 0.0 0.0 0.0 0.4 0.4
#> ASV_12 0.0 0.0 0.6 0.2 0.0 0.6 0.0 0.2 0.2
#> ASV_105 0.2 0.6 1.0 0.4 0.0 0.4 0.0 0.4 0.0
#> ASV_71 0.0 0.0 0.6 0.4 0.0 0.0 0.0 0.6 0.0
#> ASV_48 0.4 0.4 0.2 0.4 0.8 0.2 0.4 0.2 0.6
#> ASV_90 0.6 0.0 0.0 0.0 0.0 0.8 0.0 0.6 0.2
#> ASV_108 0.6 0.0 0.2 0.0 0.2 0.2 0.8 0.4 0.4
#> ASV_100 0.0 0.8 1.0 0.4 0.2 0.6 0.6 0.0 0.4
#> ASV_80 0.8 0.2 0.4 0.4 0.8 0.8 0.8 0.0 0.8
#> ASV_127 0.4 0.0 0.0 0.0 0.2 0.4 0.4 0.0 0.8
#> ASV_20 0.4 0.2 0.4 1.0 0.4 0.2 0.4 0.2 0.2
#> ASV_69 0.6 0.0 0.6 0.2 0.0 0.4 0.4 0.0 0.2
#> ASV_28 1.0 1.0 0.4 0.8 1.0 0.4 1.0 0.4 1.0
#> ASV_92 0.6 0.8 0.6 0.8 0.2 0.0 0.0 0.8 0.0
#> ASV_150 0.8 0.0 0.4 0.0 0.8 0.6 0.6 0.4 0.0
#> ASV_72 0.6 0.4 0.4 0.8 0.8 0.4 0.2 0.4 0.0
#> ASV_76 1.0 0.2 0.0 0.0 0.6 0.4 0.8 0.8 0.0
#> ASV_30 0.8 1.0 0.0 0.0 1.0 0.0 1.0 0.4 0.2
#> ASV_83 0.0 0.8 0.2 0.2 0.6 0.0 0.2 0.8 0.2
#> ASV_50 0.0 0.4 0.0 0.2 0.0 0.0 0.0 1.0 0.2
#> ASV_99 0.2 0.8 0.2 0.2 0.2 0.2 0.8 0.0 0.0
#> ASV_36 0.0 0.4 0.4 0.0 0.8 0.0 0.4 0.8 0.2
#> ASV_38 0.0 0.4 0.4 0.2 0.8 0.2 0.6 0.6 0.2
#> ASV_35 0.4 0.8 0.6 0.4 0.6 0.4 0.0 0.4 0.0
#> ASV_81 0.4 0.4 0.6 0.8 0.6 0.0 0.4 0.0 0.0
#> ASV_114 0.6 0.2 0.0 0.0 0.0 0.0 0.0 0.4 0.2
#> ASV_14 0.8 0.0 0.0 0.0 0.0 0.0 0.0 0.6 0.0
#> ASV_166 0.2 0.0 0.4 0.0 0.2 0.0 0.0 0.4 0.0
#> ASV_123 0.0 0.2 0.8 0.4 0.0 0.2 1.0 0.0 1.0
#> ASV_104 0.0 0.8 1.0 0.0 0.4 0.2 0.4 0.4 0.2
#> ASV_85 0.0 0.4 0.6 1.0 0.2 0.2 0.8 0.0 0.2
#> ASV_98 0.2 0.8 0.2 1.0 0.0 0.0 0.0 0.2 0.0
#> ASV_134 0.4 0.0 0.8 0.4 0.0 0.0 0.0 0.0 0.6
#> ASV_64 1.0 0.2 0.8 0.4 0.0 0.0 0.0 0.0 1.0
#> ASV_141 0.2 0.0 0.2 0.8 0.0 0.2 0.0 0.6 0.2
#> ASV_56 0.6 0.4 0.6 0.8 0.0 0.0 0.0 0.2 0.0
#> ASV_60 0.0 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.8
#> ASV_2 0.2 0.4 1.0 0.6 0.0 0.0 0.0 0.0 0.6
#> ASV_6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.6 0.4
#> ASV_39 0.2 0.0 0.6 0.0 0.0 0.2 0.0 0.2 0.4
#> ASV_44 0.0 0.8 0.6 0.8 0.0 0.0 0.0 0.2 0.6
#> ASV_10 0.6 0.6 1.0 0.8 0.0 0.0 0.0 0.4 0.4
#> ASV_62 0.6 0.0 0.6 0.4 0.0 0.0 0.0 0.2 0.2
#> ASV_96 0.8 0.2 0.2 0.4 0.0 0.0 0.0 0.8 0.0
#> ASV_24 0.6 0.2 0.0 0.0 0.0 0.2 0.4 1.0 0.8
#> ASV_47 0.6 0.2 1.0 0.4 0.0 0.0 0.0 0.2 0.6
#> ASV_63 0.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_79 0.2 0.0 0.0 0.0 0.0 0.0 0.4 0.0 0.2
#> ASV_17 0.0 0.0 0.8 0.0 0.0 0.0 0.4 0.4 0.6
#> ASV_41 0.0 0.2 1.0 0.6 0.0 0.0 0.0 0.0 0.2
#> ASV_73 0.0 0.2 0.8 0.6 0.0 0.0 0.4 0.4 0.8
#> ASV_120 0.0 0.0 0.0 0.0 0.0 0.8 0.0 0.4 0.2
#> ASV_137 0.0 0.0 0.8 0.0 0.0 0.2 0.0 0.2 0.2
#> ASV_130 0.0 1.0 0.8 0.0 1.0 0.0 0.0 0.8 0.8
#> ASV_107 0.0 0.6 1.0 0.6 0.2 0.6 0.4 0.6 0.8
#> ASV_115 0.0 0.6 0.4 1.0 0.4 0.0 0.0 0.2 0.0
#> ASV_118 0.2 1.0 0.0 0.0 0.0 0.0 0.6 0.0 0.2
#> ASV_70 0.0 0.6 0.0 0.0 1.0 0.8 0.4 1.0 0.6
#> ASV_103 0.0 1.0 0.8 1.0 0.6 0.8 0.8 0.6 1.0
#> ASV_133 1.0 0.0 0.4 0.6 0.2 0.0 0.0 0.2 0.6
#> ASV_23 0.0 1.0 0.0 0.0 0.2 0.4 0.2 0.0 0.8
#> ASV_42 0.4 0.0 1.0 0.0 0.6 0.8 1.0 1.0 0.8
#> ASV_45 0.6 0.0 0.0 1.0 0.6 0.8 0.2 1.0 0.8
#> ASV_140 0.2 0.2 0.6 0.6 1.0 0.0 0.0 0.2 0.2
#> ASV_97 0.0 0.4 0.8 0.8 0.0 1.0 0.0 0.4 1.0
#> ASV_29 0.0 0.2 1.0 0.2 0.0 0.0 1.0 0.6 0.2
#> ASV_147 0.2 0.0 1.0 1.0 0.2 0.4 0.6 1.0 0.0
#> ASV_54 0.6 0.8 0.8 0.8 0.2 1.0 0.2 0.0 1.0
#> ASV_15 0.0 0.4 0.8 0.0 0.0 0.6 1.0 0.0 0.0
#> ASV_156 0.0 0.2 0.4 0.6 0.0 0.6 1.0 0.0 0.0
#> ASV_132 0.4 0.6 1.0 0.8 0.0 0.6 0.0 0.6 0.6
#> ASV_22 0.0 0.6 0.6 0.8 0.4 0.2 0.6 0.6 0.0
#> ASV_68 0.6 0.8 0.8 0.6 0.6 0.8 0.8 0.6 0.6
#> ASV_59 0.6 0.6 0.4 0.0 0.0 0.6 0.0 0.2 0.4
#> ASV_87 0.6 0.6 0.0 0.0 0.0 0.4 0.0 0.8 0.8
#> ASV_74 0.2 0.2 0.4 0.0 0.0 0.0 0.2 0.0 0.0
#> ASV_65 1.0 0.6 0.0 0.6 0.2 0.8 1.0 0.4 0.8
#> ASV_66 0.8 0.2 0.8 0.8 0.0 0.0 0.0 0.4 0.4
#> ASV_101 0.2 0.2 0.6 0.8 0.0 0.0 0.0 0.8 0.0
#> ASV_122 0.4 0.2 0.8 0.8 0.0 0.0 0.0 0.2 0.6
#> ASV_163 0.4 0.2 0.8 0.8 0.0 0.0 0.0 0.2 0.6
#> ASV_32 1.0 0.0 0.6 0.8 0.0 0.4 0.0 0.4 0.0
#> ASV_128 0.6 0.2 0.0 0.0 0.0 0.0 0.0 0.6 0.0
#> ASV_33 0.8 0.4 0.0 0.2 0.0 0.2 0.0 1.0 0.0
#> ASV_77 0.8 0.4 0.4 0.4 0.0 0.0 0.0 0.8 0.0
#> ASV_159 0.8 1.0 0.2 0.8 0.2 0.0 0.2 1.0 0.0
#> ASV_46 0.6 0.2 0.2 0.6 0.0 0.6 0.2 0.4 0.0
#> ASV_112 0.4 0.2 0.6 0.4 0.0 0.2 0.0 0.0 0.0
#> ASV_89 0.0 0.8 0.2 0.8 0.6 0.0 0.0 0.6 0.2
#> ASV_111 0.4 0.4 0.8 0.6 0.2 0.8 0.0 0.4 0.0
#> ASV_37 0.2 0.6 0.6 0.8 0.0 0.6 0.8 0.8 0.0
#> ASV_1 0.4 0.8 0.4 0.0 0.0 0.4 0.8 0.2 0.4
#> ASV_86 0.6 0.8 0.0 0.8 0.0 0.2 0.8 0.8 0.8
#> ASV_18 0.0 0.4 0.4 0.6 0.8 1.0 0.6 0.4 0.8
#> ASV_34 0.8 0.8 0.8 0.8 0.0 0.6 0.4 1.0 0.0
#> ASV_25 0.0 0.6 0.4 1.0 0.4 0.4 0.6 0.4 0.8
#> ASV_124 0.6 0.4 0.4 0.2 0.4 0.8 0.4 0.4 0.0
#> ASV_119 0.2 0.8 0.2 0.8 0.2 0.6 0.8 1.0 0.0
#> ASV_153 0.2 0.4 0.0 0.0 0.6 0.6 0.6 1.0 0.2
#> ASV_52 0.2 0.4 0.2 0.2 0.0 0.2 0.4 0.8 0.2
#> ASV_21 0.0 0.0 1.0 0.0 0.6 0.2 0.8 0.0 0.6
#> ASV_49 0.6 0.0 0.4 0.0 0.2 0.4 0.4 0.6 1.0
#> ASV_53 0.0 0.4 0.0 0.0 0.4 0.0 0.6 0.6 0.8
#> ASV_138 0.8 0.8 0.8 0.4 0.2 0.8 0.6 0.8 0.6
#> ASV_16 0.0 0.8 0.4 0.6 0.6 1.0 0.4 0.4 0.2
#> ASV_95 0.6 0.6 0.6 0.6 0.4 0.2 0.2 0.8 1.0
#> ASV_9 0.8 1.0 0.2 0.6 0.0 0.2 0.8 0.2 0.6
#> ASV_135 1.0 0.0 0.6 0.2 0.0 0.0 0.0 0.8 0.4
#> ASV_31 0.2 0.0 0.0 0.0 0.2 0.4 0.4 0.8 0.2
#> ASV_91 0.6 0.6 0.2 0.8 0.6 0.2 0.0 0.0 0.4
#> ASV_67 0.8 0.0 0.0 0.0 0.0 1.0 0.4 0.6 0.2
#> ASV_43 0.2 0.0 0.6 0.0 0.2 0.0 0.0 0.4 0.2
#> ASV_172 0.0 0.0 0.6 0.0 0.0 0.0 0.0 0.8 0.2
#> ASV_51 0.8 0.6 0.8 0.6 0.6 1.0 0.4 0.0 1.0
#> ASV_117 0.6 1.0 0.4 0.6 0.0 0.4 0.2 0.0 0.0
#> ASV_8 0.8 0.6 0.8 0.8 0.0 0.2 0.0 0.2 0.0
#> ASV_106 1.0 0.4 1.0 0.6 0.0 0.0 0.0 0.0 0.0
#> ASV_125 0.8 0.6 0.0 0.6 0.6 0.0 0.4 0.6 0.6
#> ASV_113 0.4 0.6 0.2 1.0 0.0 0.0 0.0 0.6 0.2
#> ASV_109 0.8 0.8 0.4 0.4 0.0 0.0 0.0 0.0 0.4
#> ASV_26 0.2 0.0 0.4 0.6 0.0 0.0 0.0 0.0 0.6
#> ASV_154 0.8 0.4 0.2 0.2 0.0 0.0 0.0 0.4 0.6
#> ASV_88 0.0 0.8 0.4 1.0 0.0 0.0 0.0 1.0 0.6
#> ASV_151 0.0 0.0 0.6 1.0 0.0 0.0 0.0 0.4 0.6
#> ASV_27 0.8 0.4 0.2 0.0 0.0 0.2 0.0 0.8 0.4
#> ASV_148 0.8 0.4 0.2 0.2 0.0 0.0 0.0 1.0 0.6
#> ASV_162 0.0 0.2 0.8 0.4 0.0 0.0 0.0 0.8 0.2
#> ASV_142 0.8 0.0 0.0 0.0 0.4 0.0 0.0 0.4 0.8
#> ASV_121 0.6 0.0 0.0 0.0 0.0 0.2 0.0 0.4 0.6
#> ASV_126 0.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0
#> ASV_78 0.8 0.6 0.6 1.0 0.2 0.4 0.8 0.2 0.2
#> ASV_19 0.2 0.8 0.8 0.6 0.8 0.0 0.0 0.2 0.0
#> ASV_84 0.6 0.2 0.4 0.6 0.0 0.6 0.0 0.4 0.8
#> ASV_15 ASV_156 ASV_132 ASV_22 ASV_68 ASV_59 ASV_87 ASV_74 ASV_65 ASV_66
#> ASV_102 0.4 0.4 1.0 0.0 0.2 0.8 0.4 0.2 0.2 1.0
#> ASV_136 0.4 0.2 0.4 0.6 1.0 0.8 0.8 0.8 0.2 0.8
#> ASV_75 0.2 0.8 0.0 0.4 0.2 0.0 0.8 0.2 0.8 0.6
#> ASV_110 0.0 0.4 0.0 0.6 1.0 0.0 0.2 0.0 0.0 0.2
#> ASV_3 0.0 0.0 0.4 0.4 0.2 0.4 0.4 0.2 0.2 0.0
#> ASV_55 0.0 0.0 0.4 0.0 0.4 0.0 0.8 0.2 1.0 0.6
#> ASV_116 0.8 0.8 0.6 1.0 0.2 0.0 0.0 0.0 0.2 0.8
#> ASV_40 1.0 0.2 0.8 0.2 0.0 0.6 0.6 0.2 1.0 0.2
#> ASV_4 0.6 0.2 0.8 0.8 0.4 0.4 0.4 0.6 0.8 0.0
#> ASV_144 0.0 0.0 0.6 1.0 0.2 0.4 0.4 0.8 0.0 0.6
#> ASV_11 0.0 0.4 0.4 0.8 0.2 0.8 0.4 0.0 0.6 0.4
#> ASV_61 0.0 0.4 0.6 0.8 0.0 0.8 0.8 0.6 0.8 0.4
#> ASV_94 1.0 0.6 0.4 0.6 0.0 0.0 0.6 0.2 0.4 0.8
#> ASV_57 0.4 0.8 0.2 0.6 0.0 0.6 0.8 0.2 0.6 1.0
#> ASV_129 0.0 0.4 0.0 0.8 0.4 0.0 0.0 0.0 0.4 0.0
#> ASV_93 0.0 0.4 0.2 0.6 1.0 0.0 0.0 0.8 0.6 0.6
#> ASV_7 0.2 0.0 0.0 0.6 0.4 0.2 0.2 0.6 0.6 0.2
#> ASV_13 0.0 0.0 0.0 0.2 0.2 0.0 0.0 0.2 0.6 0.6
#> ASV_12 0.2 0.0 0.0 0.4 0.8 0.4 0.4 0.4 0.8 0.6
#> ASV_105 0.0 0.0 0.2 0.0 0.0 0.6 0.6 0.0 0.4 0.0
#> ASV_71 0.0 0.0 0.4 0.2 0.2 0.2 0.0 0.0 1.0 0.0
#> ASV_48 0.0 0.4 0.4 0.2 0.8 0.4 0.0 0.0 0.6 1.0
#> ASV_90 1.0 0.2 0.6 0.0 0.0 0.2 0.0 0.0 0.0 0.2
#> ASV_108 0.6 0.4 0.2 0.8 0.2 0.2 0.8 0.6 0.8 0.2
#> ASV_100 0.2 0.4 0.0 0.2 0.0 0.0 0.8 0.2 0.8 0.0
#> ASV_80 1.0 0.2 0.0 0.6 0.4 0.6 0.6 0.0 0.8 0.8
#> ASV_127 0.8 0.2 0.4 0.2 0.4 0.6 0.8 0.6 0.8 0.2
#> ASV_20 0.2 0.4 0.6 0.0 0.0 0.2 0.6 0.2 0.4 0.6
#> ASV_69 0.0 0.0 0.8 0.6 0.0 0.4 0.4 0.0 0.2 0.8
#> ASV_28 0.2 0.0 0.6 0.0 0.0 1.0 0.4 0.0 0.8 1.0
#> ASV_92 0.2 0.2 0.8 0.4 0.4 0.8 0.8 0.2 0.2 0.2
#> ASV_150 0.2 0.8 0.2 0.6 0.8 0.0 0.8 0.6 0.8 0.0
#> ASV_72 0.8 0.8 0.0 0.6 0.6 0.2 0.0 0.8 0.8 0.2
#> ASV_76 0.0 0.8 0.6 0.8 0.2 0.6 0.8 0.6 0.4 0.6
#> ASV_30 1.0 0.4 0.0 0.2 0.2 0.0 0.0 0.0 0.8 0.0
#> ASV_83 0.8 1.0 0.6 0.8 0.2 0.6 0.6 0.6 0.6 0.0
#> ASV_50 0.2 0.8 0.2 1.0 0.2 0.2 0.8 0.8 0.8 0.0
#> ASV_99 0.0 0.0 0.2 0.0 0.0 0.4 0.6 0.0 0.4 0.4
#> ASV_36 0.0 0.4 0.6 0.0 0.2 0.4 0.8 0.2 0.4 0.0
#> ASV_38 0.0 0.4 0.4 0.0 0.0 0.6 0.6 0.2 0.6 0.0
#> ASV_35 0.2 0.4 0.0 0.8 0.8 0.2 0.6 0.0 0.4 0.4
#> ASV_81 0.0 0.0 0.2 0.6 0.4 0.4 0.2 0.0 0.2 0.2
#> ASV_114 0.6 1.0 0.0 0.6 1.0 0.6 0.0 0.2 0.8 0.0
#> ASV_14 0.6 1.0 0.6 0.2 0.8 0.6 0.0 0.2 1.0 0.0
#> ASV_166 0.8 0.6 0.2 0.8 0.6 0.6 0.4 0.4 0.4 0.0
#> ASV_123 0.2 0.0 0.2 1.0 1.0 1.0 0.4 0.0 0.0 0.0
#> ASV_104 0.2 0.0 0.4 0.4 0.8 0.8 0.2 0.4 0.8 0.0
#> ASV_85 0.2 0.2 0.4 0.6 1.0 0.0 0.0 0.6 0.8 0.0
#> ASV_98 0.0 0.0 0.2 0.2 0.6 0.2 0.0 0.0 1.0 0.0
#> ASV_134 0.2 0.0 0.4 1.0 0.2 0.6 0.0 0.0 0.8 0.0
#> ASV_64 0.6 1.0 0.0 0.6 0.6 0.0 0.0 0.0 0.6 0.0
#> ASV_141 0.6 0.6 0.8 0.8 0.8 0.2 0.2 0.8 0.6 0.0
#> ASV_56 0.6 0.0 0.6 0.0 0.8 0.0 0.2 0.0 0.4 0.0
#> ASV_60 0.4 0.2 0.0 0.2 0.2 0.6 0.4 0.2 0.8 0.0
#> ASV_2 0.6 0.4 0.4 0.8 0.2 0.4 0.2 1.0 0.4 0.0
#> ASV_6 0.2 0.0 1.0 0.6 0.8 0.0 0.2 0.2 0.6 0.0
#> ASV_39 0.6 0.2 0.4 0.4 0.2 0.4 0.2 0.2 0.8 0.0
#> ASV_44 0.6 0.8 0.0 0.4 0.4 0.0 0.2 0.0 0.6 0.0
#> ASV_10 0.4 0.2 0.4 1.0 0.6 0.0 0.0 0.0 0.0 0.0
#> ASV_62 0.0 0.0 0.2 0.0 0.2 0.0 0.2 0.2 0.6 0.0
#> ASV_96 0.2 0.0 0.0 0.0 0.6 0.0 0.0 0.0 1.0 0.0
#> ASV_24 0.6 0.0 0.2 0.0 0.4 0.4 0.0 0.2 0.6 0.0
#> ASV_47 0.4 0.0 0.4 0.8 0.4 1.0 0.8 0.6 0.2 0.0
#> ASV_63 0.2 0.0 1.0 0.2 0.2 0.6 0.4 0.0 0.6 0.0
#> ASV_79 0.0 0.0 0.6 0.2 0.2 0.6 0.2 0.6 0.6 0.0
#> ASV_17 0.6 0.6 1.0 0.6 0.2 0.4 0.4 0.6 1.0 0.0
#> ASV_41 0.6 0.2 0.8 0.6 0.2 0.6 0.2 0.0 0.4 0.0
#> ASV_73 0.0 0.0 0.2 0.0 0.4 0.6 0.2 0.2 0.4 0.0
#> ASV_120 0.2 0.0 0.6 0.0 1.0 0.0 0.4 0.6 0.8 0.0
#> ASV_137 0.0 0.0 0.2 0.2 0.4 0.0 0.0 0.0 0.8 0.0
#> ASV_130 0.8 0.6 1.0 0.2 0.0 0.2 0.2 0.4 0.6 0.6
#> ASV_107 0.0 0.0 0.4 0.0 0.4 0.6 0.2 0.2 0.6 0.2
#> ASV_115 0.0 0.0 0.2 0.0 0.6 0.2 0.4 0.0 0.0 0.0
#> ASV_118 1.0 0.4 0.0 0.4 0.8 0.2 0.0 0.8 0.4 0.0
#> ASV_70 0.2 0.4 0.6 0.0 0.6 0.4 1.0 0.2 0.8 0.4
#> ASV_103 0.4 0.6 0.8 0.6 0.2 1.0 0.6 0.8 0.0 0.6
#> ASV_133 0.0 0.0 0.4 0.0 0.6 0.6 0.6 0.2 1.0 0.8
#> ASV_23 0.4 0.2 0.6 0.6 0.8 0.6 0.6 0.2 0.6 0.2
#> ASV_42 0.8 0.4 1.0 0.6 0.8 0.4 0.0 0.4 0.0 0.8
#> ASV_45 0.0 0.6 0.8 0.8 0.6 0.0 0.0 0.0 0.6 0.8
#> ASV_140 0.0 0.0 0.0 0.4 0.6 0.0 0.0 0.0 0.2 0.0
#> ASV_97 0.6 0.6 0.6 0.2 0.8 0.6 0.4 0.0 0.8 0.0
#> ASV_29 1.0 1.0 0.0 0.6 0.8 0.0 0.0 0.2 1.0 0.0
#> ASV_147 0.0 0.0 0.6 0.6 0.6 0.2 0.8 0.0 0.4 0.4
#> ASV_54 0.0 0.0 0.6 0.0 0.6 0.4 0.8 0.0 0.8 0.4
#> ASV_15 1.0 0.0 0.2 0.0 0.8 0.0 0.0 0.0 0.8 0.4
#> ASV_156 0.0 1.0 0.8 0.0 0.4 0.2 0.8 0.0 0.2 0.4
#> ASV_132 0.2 0.8 1.0 0.6 0.0 0.0 0.4 0.4 0.4 0.0
#> ASV_22 0.0 0.0 0.6 1.0 0.0 0.8 0.4 0.0 0.6 1.0
#> ASV_68 0.8 0.4 0.0 0.0 1.0 0.2 0.6 0.0 0.2 0.6
#> ASV_59 0.0 0.2 0.0 0.8 0.2 1.0 0.0 0.0 0.0 0.2
#> ASV_87 0.0 0.8 0.4 0.4 0.6 0.0 1.0 0.0 0.0 0.0
#> ASV_74 0.0 0.0 0.4 0.0 0.0 0.0 0.0 1.0 0.0 0.2
#> ASV_65 0.8 0.2 0.4 0.6 0.2 0.0 0.0 0.0 1.0 1.0
#> ASV_66 0.4 0.4 0.0 1.0 0.6 0.2 0.0 0.2 1.0 1.0
#> ASV_101 0.6 0.6 0.0 1.0 0.8 0.0 0.0 0.8 0.4 0.0
#> ASV_122 1.0 0.6 1.0 0.6 0.8 0.0 0.2 0.0 0.0 0.0
#> ASV_163 0.2 0.2 0.6 0.6 0.4 0.2 0.2 0.0 1.0 0.0
#> ASV_32 0.6 0.6 0.0 0.6 0.0 0.0 0.0 0.2 0.2 0.0
#> ASV_128 0.8 0.4 0.2 1.0 0.2 0.0 0.2 0.2 0.6 0.0
#> ASV_33 0.8 0.0 0.0 1.0 0.4 0.2 0.0 0.2 1.0 0.0
#> ASV_77 0.8 0.2 0.0 1.0 0.2 0.0 0.0 0.2 0.8 0.0
#> ASV_159 0.0 0.0 0.0 0.0 0.6 0.0 0.0 0.0 0.2 0.0
#> ASV_46 0.0 0.0 0.8 0.6 0.8 0.8 0.0 0.4 0.6 0.0
#> ASV_112 0.0 0.0 1.0 0.0 0.4 0.0 0.0 0.0 0.6 0.0
#> ASV_89 0.8 0.6 0.2 0.8 0.8 0.2 0.0 1.0 0.8 0.0
#> ASV_111 0.8 0.4 0.4 1.0 0.4 0.2 0.0 0.6 0.4 0.0
#> ASV_37 1.0 0.2 0.4 0.8 0.2 0.6 0.2 0.6 0.6 0.0
#> ASV_1 0.2 0.0 0.8 0.0 0.4 0.2 0.6 0.0 0.6 0.0
#> ASV_86 0.6 0.2 0.2 0.0 0.8 0.2 0.4 1.0 0.8 0.0
#> ASV_18 0.0 0.0 0.8 0.0 0.0 0.0 0.0 0.6 0.4 0.4
#> ASV_34 0.0 0.0 0.8 0.0 1.0 0.6 0.2 0.4 0.6 0.0
#> ASV_25 0.0 0.8 0.8 1.0 0.2 0.2 0.6 0.4 0.4 0.4
#> ASV_124 1.0 0.4 1.0 0.4 0.4 0.6 0.4 0.4 0.6 0.4
#> ASV_119 0.6 0.0 1.0 0.6 0.6 1.0 0.4 0.6 0.8 0.2
#> ASV_153 0.2 0.8 1.0 0.6 0.4 0.8 0.4 0.4 0.6 0.4
#> ASV_52 1.0 0.4 0.8 0.2 0.4 0.8 0.8 0.2 0.4 0.2
#> ASV_21 0.2 0.8 0.8 0.0 0.2 1.0 0.2 0.4 0.2 0.2
#> ASV_49 0.0 0.8 0.8 0.4 0.6 0.4 0.8 0.2 0.0 0.2
#> ASV_53 0.2 0.6 0.0 0.6 0.8 0.4 1.0 0.6 0.4 0.2
#> ASV_138 0.4 0.4 0.0 0.4 1.0 0.0 0.4 0.0 0.0 0.6
#> ASV_16 0.6 0.2 0.0 0.2 0.2 0.0 0.8 0.0 0.0 0.6
#> ASV_95 0.2 0.2 0.0 0.2 1.0 0.2 0.4 0.0 0.0 0.6
#> ASV_9 0.8 0.2 0.8 0.0 0.2 0.2 0.8 0.4 1.0 0.2
#> ASV_135 0.0 0.0 0.4 0.0 0.4 0.4 0.2 0.0 0.8 0.0
#> ASV_31 0.0 0.0 0.2 0.0 0.0 1.0 0.6 0.2 0.6 0.4
#> ASV_91 0.4 1.0 0.2 0.4 0.2 0.2 0.2 0.4 0.4 0.6
#> ASV_67 0.8 1.0 0.0 0.6 0.4 0.0 0.8 0.4 0.4 0.0
#> ASV_43 0.6 0.4 0.0 0.6 0.6 0.0 0.6 0.4 0.8 0.0
#> ASV_172 0.8 0.4 0.0 0.6 0.6 0.0 0.0 0.4 1.0 0.0
#> ASV_51 0.4 0.4 0.2 0.6 0.0 0.2 0.4 0.2 0.0 0.8
#> ASV_117 0.0 0.0 0.4 0.2 0.4 1.0 1.0 0.0 0.0 0.2
#> ASV_8 0.0 0.0 0.4 0.0 0.0 0.2 0.0 0.0 0.0 0.0
#> ASV_106 0.0 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_125 0.2 0.2 0.0 0.0 0.0 0.0 0.6 0.8 0.0 0.0
#> ASV_113 0.4 0.8 0.0 0.0 0.0 0.8 1.0 0.8 0.8 0.0
#> ASV_109 0.8 0.0 0.0 0.8 0.6 0.0 0.0 0.0 0.4 0.0
#> ASV_26 0.2 0.0 0.8 0.8 0.2 1.0 0.0 0.0 0.2 0.0
#> ASV_154 1.0 0.8 0.0 1.0 0.0 0.4 0.8 0.2 0.2 0.2
#> ASV_88 0.8 0.4 0.0 0.8 0.4 0.0 0.0 0.0 0.4 0.0
#> ASV_151 0.0 0.6 0.4 1.0 0.8 0.0 0.0 0.0 1.0 0.0
#> ASV_27 0.0 0.0 0.4 0.0 0.0 0.8 0.4 0.8 0.0 0.0
#> ASV_148 0.0 0.0 0.0 0.0 0.0 0.2 0.8 0.8 0.6 0.0
#> ASV_162 0.4 0.8 0.8 0.0 0.0 0.4 0.0 0.6 0.6 0.2
#> ASV_142 0.8 1.0 0.2 0.8 0.0 0.0 0.0 0.4 0.2 0.0
#> ASV_121 0.8 0.6 0.4 1.0 0.4 0.4 0.0 0.6 1.0 0.0
#> ASV_126 0.4 0.4 0.6 0.8 0.2 0.2 0.2 0.0 0.2 0.0
#> ASV_78 0.0 0.0 0.0 0.4 0.6 0.8 0.2 0.2 1.0 0.8
#> ASV_19 0.6 0.2 0.4 0.4 0.2 0.4 0.4 0.4 0.2 0.0
#> ASV_84 1.0 0.2 0.4 0.4 0.8 0.0 0.2 0.2 0.0 0.0
#> ASV_101 ASV_122 ASV_163 ASV_32 ASV_128 ASV_33 ASV_77 ASV_159 ASV_46
#> ASV_102 1.0 0.4 0.8 0.6 0.6 0.8 0.4 1.0 0.4
#> ASV_136 0.6 0.0 0.6 0.4 0.4 0.6 0.8 0.2 0.8
#> ASV_75 0.6 0.2 0.6 0.6 0.8 0.0 0.2 0.0 0.6
#> ASV_110 0.0 0.0 0.6 0.6 0.4 0.2 0.0 0.2 0.8
#> ASV_3 0.0 0.0 0.0 1.0 0.2 0.4 0.0 0.6 0.8
#> ASV_55 0.0 0.0 0.8 0.4 0.6 0.0 0.2 0.4 0.0
#> ASV_116 0.8 0.4 0.8 0.4 0.4 0.2 0.0 0.6 0.2
#> ASV_40 0.8 0.4 0.2 0.8 0.2 0.4 0.8 0.8 1.0
#> ASV_4 0.4 0.0 0.0 0.6 0.0 0.0 0.0 0.6 0.4
#> ASV_144 0.6 1.0 0.4 0.8 0.8 0.4 0.8 0.6 0.6
#> ASV_11 0.2 0.0 0.0 1.0 0.0 0.6 0.4 0.8 1.0
#> ASV_61 0.2 0.2 0.0 0.6 0.0 0.6 0.4 0.8 1.0
#> ASV_94 0.0 0.2 0.0 0.8 0.4 0.4 0.8 0.8 0.4
#> ASV_57 0.4 0.2 0.4 1.0 0.2 0.6 0.2 1.0 0.8
#> ASV_129 0.0 0.0 0.0 0.0 0.4 0.4 0.0 0.0 0.4
#> ASV_93 0.0 0.0 0.8 1.0 1.0 0.4 0.2 0.2 0.8
#> ASV_7 0.0 0.8 0.4 0.0 0.0 0.0 0.0 0.2 0.2
#> ASV_13 0.0 0.4 0.6 0.2 0.0 0.2 0.0 0.6 0.2
#> ASV_12 0.0 0.6 0.4 0.6 0.2 0.8 0.8 1.0 0.2
#> ASV_105 0.0 0.0 0.0 0.2 0.0 0.2 0.2 0.0 0.0
#> ASV_71 0.0 0.0 0.0 0.0 0.0 0.2 0.0 0.2 0.0
#> ASV_48 0.8 0.6 1.0 0.6 0.8 0.8 1.0 0.2 0.2
#> ASV_90 0.6 0.2 0.2 0.6 0.4 0.4 0.6 0.2 0.6
#> ASV_108 0.2 0.0 0.6 0.2 0.4 0.2 0.4 0.2 0.6
#> ASV_100 0.8 0.0 0.0 0.6 0.0 0.4 0.4 0.4 0.4
#> ASV_80 0.2 0.0 0.2 0.8 0.4 0.8 0.6 0.8 1.0
#> ASV_127 0.6 0.2 0.0 0.0 0.0 0.8 0.0 1.0 1.0
#> ASV_20 0.8 0.4 0.4 0.2 0.2 0.0 0.0 0.8 0.4
#> ASV_69 0.8 0.4 0.4 0.8 0.8 0.8 0.8 1.0 0.2
#> ASV_28 1.0 0.8 1.0 0.8 0.6 0.4 0.2 0.0 0.2
#> ASV_92 1.0 0.2 0.2 0.8 0.2 0.4 0.6 0.2 0.0
#> ASV_150 0.4 0.0 0.0 0.4 0.6 0.2 0.2 0.4 0.0
#> ASV_72 0.8 0.8 1.0 0.6 0.2 0.0 0.0 0.0 0.0
#> ASV_76 0.2 0.4 0.6 0.6 0.6 0.6 0.4 0.6 0.4
#> ASV_30 0.0 0.2 0.0 0.2 0.0 0.0 0.2 0.8 0.8
#> ASV_83 0.2 0.0 0.0 0.2 0.2 0.0 0.2 0.4 0.4
#> ASV_50 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.4
#> ASV_99 0.2 0.8 0.0 0.2 0.8 0.6 0.2 0.8 0.4
#> ASV_36 0.2 0.2 0.0 0.4 0.0 0.0 0.4 0.8 0.8
#> ASV_38 0.0 0.2 0.0 0.6 0.4 0.0 0.0 0.6 0.6
#> ASV_35 0.0 0.2 0.4 0.8 0.0 0.4 0.0 0.8 0.2
#> ASV_81 0.0 0.4 0.2 0.6 1.0 0.2 0.2 0.8 0.6
#> ASV_114 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.0 0.2
#> ASV_14 0.0 0.0 0.0 0.4 0.0 0.4 0.6 0.4 0.8
#> ASV_166 0.0 0.0 0.0 0.2 0.4 0.2 0.4 1.0 0.8
#> ASV_123 0.4 0.0 0.0 0.4 0.2 0.2 0.2 0.2 0.0
#> ASV_104 0.6 0.0 0.2 0.0 0.2 0.4 0.0 0.0 0.0
#> ASV_85 1.0 0.2 0.4 0.0 0.0 0.0 0.0 0.4 0.0
#> ASV_98 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_134 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.6
#> ASV_141 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_56 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_60 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_39 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_44 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_10 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_62 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_96 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2
#> ASV_24 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2
#> ASV_47 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_63 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_79 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_17 0.0 0.0 0.0 0.2 0.0 0.0 0.2 0.2 0.0
#> ASV_41 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_73 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.0 0.0
#> ASV_120 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0
#> ASV_137 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.0
#> ASV_130 0.2 0.2 0.8 0.2 0.0 0.2 0.8 0.4 0.4
#> ASV_107 0.4 0.0 0.0 0.4 0.8 0.8 1.0 0.6 0.0
#> ASV_115 0.0 0.8 0.4 0.0 0.4 1.0 1.0 0.6 0.0
#> ASV_118 0.4 0.4 0.4 0.4 0.8 0.0 0.8 0.6 0.0
#> ASV_70 0.2 0.4 0.6 1.0 0.8 1.0 1.0 0.6 0.2
#> ASV_103 1.0 0.4 0.6 0.6 0.6 0.4 0.4 1.0 0.6
#> ASV_133 0.2 0.4 0.4 1.0 0.6 0.8 0.8 0.8 0.6
#> ASV_23 0.2 0.2 0.2 0.0 0.2 0.4 0.4 1.0 0.2
#> ASV_42 0.6 0.8 0.8 0.6 0.0 0.0 0.4 0.2 0.2
#> ASV_45 0.8 0.8 0.8 0.8 0.0 0.2 0.4 0.8 0.6
#> ASV_140 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.0
#> ASV_97 0.0 0.0 0.0 0.4 0.0 0.2 0.0 0.0 0.6
#> ASV_29 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.2
#> ASV_147 0.8 0.2 0.2 0.4 0.6 1.0 0.8 1.0 0.4
#> ASV_54 0.0 0.6 0.6 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_15 0.6 1.0 0.2 0.6 0.8 0.8 0.8 0.0 0.0
#> ASV_156 0.6 0.6 0.2 0.6 0.4 0.0 0.2 0.0 0.0
#> ASV_132 0.0 1.0 0.6 0.0 0.2 0.0 0.0 0.0 0.8
#> ASV_22 1.0 0.6 0.6 0.6 1.0 1.0 1.0 0.0 0.6
#> ASV_68 0.8 0.8 0.4 0.0 0.2 0.4 0.2 0.6 0.8
#> ASV_59 0.0 0.0 0.2 0.0 0.0 0.2 0.0 0.0 0.8
#> ASV_87 0.0 0.2 0.2 0.0 0.2 0.0 0.0 0.0 0.0
#> ASV_74 0.8 0.0 0.0 0.2 0.2 0.2 0.2 0.0 0.4
#> ASV_65 0.4 0.0 1.0 0.2 0.6 1.0 0.8 0.2 0.6
#> ASV_66 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_101 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.4
#> ASV_122 0.0 1.0 0.0 0.2 0.0 0.2 0.0 0.2 0.4
#> ASV_163 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.2 0.4
#> ASV_32 0.0 0.2 0.0 1.0 0.0 0.0 0.0 0.0 0.0
#> ASV_128 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0
#> ASV_33 0.0 0.2 0.0 0.0 0.0 1.0 0.0 0.0 0.0
#> ASV_77 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0
#> ASV_159 0.2 0.2 0.2 0.0 0.0 0.0 0.0 1.0 0.0
#> ASV_46 0.4 0.4 0.4 0.0 0.0 0.0 0.0 0.0 1.0
#> ASV_112 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_89 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_111 0.0 0.2 0.2 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_37 0.0 0.6 0.2 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_1 1.0 0.0 0.0 0.6 0.2 0.0 0.4 0.0 0.0
#> ASV_86 0.0 0.0 0.0 0.2 0.0 0.0 0.0 0.0 0.6
#> ASV_18 0.6 0.6 0.8 0.6 0.6 0.4 0.8 0.0 0.2
#> ASV_34 0.4 0.4 0.2 0.2 0.2 0.2 0.4 0.0 0.0
#> ASV_25 0.6 0.6 0.6 0.6 0.2 0.4 0.2 0.6 0.6
#> ASV_124 1.0 0.6 1.0 0.0 0.2 0.4 0.0 0.4 0.4
#> ASV_119 0.4 0.2 0.0 0.2 0.0 0.0 0.0 0.4 0.0
#> ASV_153 0.8 0.2 0.6 0.4 0.2 0.2 0.2 0.6 0.4
#> ASV_52 0.6 0.0 0.0 0.2 0.0 0.2 0.2 0.2 0.0
#> ASV_21 0.8 0.0 0.4 1.0 0.2 0.2 0.4 0.2 0.4
#> ASV_49 0.2 0.0 0.2 0.2 0.0 0.0 0.2 0.4 1.0
#> ASV_53 0.6 0.0 0.4 0.4 0.4 0.4 0.4 0.4 0.4
#> ASV_138 0.2 0.0 0.8 0.6 0.8 0.8 0.8 0.4 0.8
#> ASV_16 0.2 0.2 0.6 0.2 0.8 0.8 0.6 0.4 0.4
#> ASV_95 0.6 0.6 0.8 0.6 0.6 0.8 0.6 0.4 0.8
#> ASV_9 0.0 0.0 0.2 0.6 0.6 0.4 0.8 0.0 0.4
#> ASV_135 0.0 0.0 0.0 0.4 0.2 0.2 0.4 0.2 0.4
#> ASV_31 0.6 0.2 0.4 0.8 0.4 0.4 0.4 0.0 0.2
#> ASV_91 0.2 0.0 0.6 0.6 0.4 0.8 0.4 0.4 0.2
#> ASV_67 0.0 0.4 0.0 0.6 0.6 0.8 0.4 0.6 0.6
#> ASV_43 0.0 0.0 0.0 0.0 0.2 0.0 0.2 0.4 0.0
#> ASV_172 0.0 0.0 0.0 0.0 0.2 0.0 0.0 0.4 0.2
#> ASV_51 0.8 0.2 0.6 1.0 0.6 1.0 0.8 0.4 0.6
#> ASV_117 0.4 0.0 0.4 0.4 0.8 1.0 0.6 0.0 0.2
#> ASV_8 0.0 0.0 0.0 0.4 0.4 0.2 0.2 0.0 0.0
#> ASV_106 0.2 0.0 0.0 0.0 0.6 0.2 0.0 0.0 0.2
#> ASV_125 1.0 0.0 0.0 0.0 0.6 0.4 0.2 0.4 0.0
#> ASV_113 0.0 0.0 0.0 1.0 0.0 0.2 0.6 0.8 0.4
#> ASV_109 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_26 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.4 0.0
#> ASV_154 0.6 0.0 0.0 0.8 0.2 0.6 0.0 0.8 1.0
#> ASV_88 0.0 0.0 0.0 0.0 0.8 0.8 0.2 0.8 0.6
#> ASV_151 0.0 0.0 0.2 0.0 0.2 1.0 0.0 0.2 0.2
#> ASV_27 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.6 0.4
#> ASV_148 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.6
#> ASV_162 0.2 0.0 0.0 0.0 0.0 0.2 0.2 0.8 0.6
#> ASV_142 0.2 0.2 0.4 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_121 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.4
#> ASV_126 0.0 0.0 0.0 0.2 0.2 0.0 0.0 0.0 0.8
#> ASV_78 0.2 0.6 0.6 0.8 0.6 0.2 0.8 0.0 1.0
#> ASV_19 0.0 0.4 0.0 0.2 0.2 0.4 0.2 0.4 0.6
#> ASV_84 0.0 0.0 0.0 0.8 0.4 0.0 0.8 0.2 1.0
#> ASV_112 ASV_89 ASV_111 ASV_37 ASV_1 ASV_86 ASV_18 ASV_34 ASV_25 ASV_124
#> ASV_102 0.6 0.8 0.2 0.0 0.8 0.8 0.0 0.4 0.6 0.2
#> ASV_136 0.0 0.2 0.2 0.0 0.8 0.4 0.6 0.8 0.6 0.4
#> ASV_75 0.6 0.4 0.2 0.2 0.8 0.0 0.8 1.0 1.0 0.8
#> ASV_110 1.0 0.8 0.6 0.6 0.8 0.8 1.0 0.8 0.6 0.8
#> ASV_3 0.0 0.6 0.4 0.0 0.8 1.0 0.6 0.8 0.4 1.0
#> ASV_55 0.0 0.0 0.0 0.0 0.2 1.0 0.2 0.0 0.2 0.0
#> ASV_116 0.4 0.2 0.6 0.8 0.8 0.2 0.6 0.4 0.2 0.0
#> ASV_40 0.8 1.0 0.8 0.4 0.0 0.0 0.0 0.4 1.0 0.4
#> ASV_4 0.4 0.0 0.6 0.0 0.6 0.4 0.2 0.6 0.2 0.8
#> ASV_144 0.2 0.4 0.4 0.6 0.8 0.8 0.8 0.6 0.0 0.2
#> ASV_11 1.0 1.0 0.8 0.2 0.6 0.6 1.0 0.6 0.6 0.6
#> ASV_61 1.0 1.0 0.6 0.0 0.4 1.0 1.0 0.6 0.4 0.2
#> ASV_94 0.6 0.2 0.6 0.0 0.2 0.4 0.2 0.4 0.6 0.6
#> ASV_57 1.0 0.6 0.8 0.0 0.4 0.6 0.6 0.4 0.6 1.0
#> ASV_129 0.0 0.8 1.0 1.0 0.0 1.0 0.4 0.2 0.6 0.6
#> ASV_93 1.0 0.6 0.4 0.0 0.4 0.6 0.4 0.6 0.4 0.6
#> ASV_7 1.0 0.0 0.6 0.2 0.8 0.4 1.0 0.8 0.8 0.2
#> ASV_13 0.6 0.4 0.2 0.2 0.8 1.0 0.8 0.4 0.4 0.0
#> ASV_12 0.0 0.6 0.8 0.4 0.8 0.8 0.2 0.4 0.0 0.6
#> ASV_105 0.0 0.4 0.4 0.8 0.2 1.0 0.0 0.0 0.4 0.2
#> ASV_71 0.2 0.2 1.0 0.4 0.0 0.6 0.8 0.4 0.8 0.8
#> ASV_48 1.0 0.8 1.0 0.0 0.0 0.4 0.4 0.0 0.8 0.2
#> ASV_90 0.4 0.8 0.2 0.0 0.0 0.2 0.6 0.8 0.6 0.0
#> ASV_108 0.8 0.8 0.8 0.6 0.0 0.0 0.6 0.0 0.0 0.4
#> ASV_100 0.4 0.4 0.6 0.8 0.0 0.0 0.4 0.0 1.0 0.6
#> ASV_80 0.4 0.8 0.8 0.2 0.0 1.0 0.6 0.4 0.8 0.8
#> ASV_127 0.0 0.2 0.8 0.6 0.2 0.6 0.0 0.4 0.0 0.8
#> ASV_20 0.2 0.2 0.2 0.8 0.2 0.0 0.0 0.2 0.6 0.2
#> ASV_69 0.8 0.6 0.4 0.0 0.4 1.0 0.4 0.2 0.4 0.6
#> ASV_28 0.4 0.4 0.6 1.0 0.0 0.0 0.0 0.0 0.6 0.4
#> ASV_92 0.2 0.4 0.6 0.8 0.2 0.2 0.2 0.0 0.8 0.6
#> ASV_150 0.8 0.8 0.2 0.0 0.0 0.4 0.2 0.6 0.2 0.2
#> ASV_72 0.0 0.0 0.0 0.0 0.8 0.2 0.6 0.4 0.4 0.0
#> ASV_76 0.6 0.4 0.6 1.0 0.8 0.4 0.2 1.0 0.4 0.2
#> ASV_30 0.2 0.0 0.2 0.4 0.4 0.0 0.8 0.2 0.0 0.8
#> ASV_83 0.4 0.0 0.0 0.4 0.4 0.4 0.6 0.0 0.0 0.4
#> ASV_50 0.2 0.0 0.0 0.0 0.6 0.2 0.6 0.4 0.2 1.0
#> ASV_99 0.4 0.4 0.2 0.6 0.8 0.2 0.2 0.4 0.0 0.8
#> ASV_36 1.0 0.2 0.2 0.6 0.4 0.2 0.2 0.4 0.0 0.6
#> ASV_38 0.4 0.6 0.4 0.8 0.6 0.2 0.2 0.6 0.0 0.0
#> ASV_35 0.2 0.6 0.4 0.2 0.8 0.8 0.4 0.6 0.4 0.0
#> ASV_81 0.0 0.0 0.2 0.6 0.4 0.0 0.8 0.8 0.2 0.6
#> ASV_114 0.0 0.0 0.2 0.8 0.6 0.4 0.6 0.2 0.6 1.0
#> ASV_14 0.8 0.0 0.6 0.2 1.0 0.4 0.4 0.8 0.4 0.2
#> ASV_166 1.0 0.4 0.6 0.0 0.6 1.0 1.0 0.8 0.6 0.4
#> ASV_123 0.0 0.0 0.0 0.2 0.2 0.6 1.0 0.0 0.0 0.8
#> ASV_104 0.0 0.0 0.0 0.0 0.0 0.0 0.6 0.0 0.2 0.4
#> ASV_85 0.0 0.0 0.0 0.0 0.6 1.0 0.8 0.6 0.0 0.0
#> ASV_98 0.0 0.0 0.0 0.2 0.0 0.6 0.8 0.0 0.0 0.4
#> ASV_134 0.0 0.0 0.0 0.0 0.2 0.6 0.6 0.6 0.4 0.6
#> ASV_64 0.0 0.0 0.0 0.0 0.4 0.6 0.4 0.6 0.4 0.6
#> ASV_141 0.0 0.0 0.0 0.0 0.2 0.0 0.4 0.0 0.4 0.8
#> ASV_56 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.6 0.4
#> ASV_60 0.0 0.0 0.0 0.0 0.2 0.0 0.2 0.2 0.0 0.8
#> ASV_2 0.0 0.0 0.0 0.0 0.0 0.0 0.6 0.2 0.2 0.4
#> ASV_6 0.0 0.0 0.0 0.0 0.2 0.0 0.4 0.0 0.4 0.6
#> ASV_39 0.0 0.0 0.0 0.0 0.2 0.0 0.4 0.0 0.2 0.4
#> ASV_44 0.0 0.0 0.0 0.0 0.2 0.2 0.8 0.4 0.4 0.6
#> ASV_10 0.0 0.0 0.2 0.0 0.0 0.0 0.6 0.2 0.2 0.4
#> ASV_62 0.0 0.2 0.4 0.2 0.0 0.2 0.4 0.0 0.4 0.4
#> ASV_96 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.4 0.6
#> ASV_24 0.0 0.0 0.2 0.2 0.0 0.0 0.0 0.0 0.4 0.6
#> ASV_47 0.0 0.2 0.0 0.0 0.4 0.0 0.0 0.0 0.4 0.6
#> ASV_63 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2 1.0
#> ASV_79 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.4 0.6
#> ASV_17 0.0 0.0 0.0 0.0 0.2 0.0 1.0 0.0 0.2 0.8
#> ASV_41 0.0 0.0 0.0 0.0 0.0 0.0 0.8 0.0 0.2 0.8
#> ASV_73 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.4 1.0
#> ASV_120 0.0 0.0 0.0 0.0 0.2 0.0 0.0 0.0 1.0 0.8
#> ASV_137 0.0 0.0 0.0 0.2 0.8 0.8 0.2 0.2 0.6 1.0
#> ASV_130 0.8 0.2 0.6 0.8 1.0 0.6 0.6 0.6 0.6 0.4
#> ASV_107 0.0 0.0 0.0 0.6 0.4 0.0 0.0 0.0 0.8 0.4
#> ASV_115 0.0 1.0 0.6 0.8 0.4 0.8 0.4 0.4 0.4 0.8
#> ASV_118 1.0 0.8 0.6 0.2 0.0 0.4 0.2 0.4 0.2 0.0
#> ASV_70 0.4 0.0 0.6 0.8 0.0 0.0 0.0 0.0 0.2 0.4
#> ASV_103 0.6 0.6 0.4 0.6 1.0 1.0 0.4 0.6 0.0 0.0
#> ASV_133 0.4 0.0 0.4 0.2 0.4 0.6 0.0 0.8 0.0 0.6
#> ASV_23 0.2 0.8 0.4 0.6 0.8 0.8 0.4 0.8 0.6 0.4
#> ASV_42 0.6 0.2 0.8 0.6 0.4 0.0 0.4 0.8 0.4 0.4
#> ASV_45 0.4 0.8 0.6 0.8 0.0 0.8 0.6 0.8 1.0 0.2
#> ASV_140 0.0 0.6 0.2 0.0 0.0 0.0 0.8 0.0 0.4 0.4
#> ASV_97 0.2 0.0 0.8 0.6 0.4 0.2 1.0 0.6 0.4 0.8
#> ASV_29 0.0 0.0 0.0 0.8 0.8 0.8 0.6 0.4 0.6 0.4
#> ASV_147 0.0 0.6 0.4 0.8 0.2 0.8 0.4 1.0 0.4 0.4
#> ASV_54 0.0 0.2 0.0 0.0 0.4 0.8 0.8 0.0 0.8 0.0
#> ASV_15 0.0 0.8 0.8 1.0 0.2 0.6 0.0 0.0 0.0 1.0
#> ASV_156 0.0 0.6 0.4 0.2 0.0 0.2 0.0 0.0 0.8 0.4
#> ASV_132 1.0 0.2 0.4 0.4 0.8 0.2 0.8 0.8 0.8 1.0
#> ASV_22 0.0 0.8 1.0 0.8 0.0 0.0 0.0 0.0 1.0 0.4
#> ASV_68 0.4 0.8 0.4 0.2 0.4 0.8 0.0 1.0 0.2 0.4
#> ASV_59 0.0 0.2 0.2 0.6 0.2 0.2 0.0 0.6 0.2 0.6
#> ASV_87 0.0 0.0 0.0 0.2 0.6 0.4 0.0 0.2 0.6 0.4
#> ASV_74 0.0 1.0 0.6 0.6 0.0 1.0 0.6 0.4 0.4 0.4
#> ASV_65 0.6 0.8 0.4 0.6 0.6 0.8 0.4 0.6 0.4 0.6
#> ASV_66 0.0 0.0 0.0 0.0 0.0 0.0 0.4 0.0 0.4 0.4
#> ASV_101 0.0 0.0 0.0 0.0 1.0 0.0 0.6 0.4 0.6 1.0
#> ASV_122 0.0 0.0 0.2 0.6 0.0 0.0 0.6 0.4 0.6 0.6
#> ASV_163 0.0 0.0 0.2 0.2 0.0 0.0 0.8 0.2 0.6 1.0
#> ASV_32 0.0 0.0 0.0 0.0 0.6 0.2 0.6 0.2 0.6 0.0
#> ASV_128 0.0 0.0 0.0 0.0 0.2 0.0 0.6 0.2 0.2 0.2
#> ASV_33 0.0 0.0 0.0 0.0 0.0 0.0 0.4 0.2 0.4 0.4
#> ASV_77 0.0 0.0 0.0 0.0 0.4 0.0 0.8 0.4 0.2 0.0
#> ASV_159 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.6 0.4
#> ASV_46 0.0 0.0 0.0 0.0 0.0 0.6 0.2 0.0 0.6 0.4
#> ASV_112 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.6 0.4
#> ASV_89 0.0 1.0 0.0 0.0 0.0 0.0 0.2 0.2 0.6 0.4
#> ASV_111 0.0 0.0 1.0 0.0 0.2 0.0 0.0 0.2 0.6 0.6
#> ASV_37 0.0 0.0 0.0 1.0 0.0 0.0 0.8 0.2 0.4 0.0
#> ASV_1 0.0 0.0 0.2 0.0 1.0 0.0 0.2 0.0 0.6 0.4
#> ASV_86 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.6 0.6
#> ASV_18 0.0 0.2 0.0 0.8 0.2 0.0 1.0 0.0 1.0 0.4
#> ASV_34 0.0 0.2 0.2 0.2 0.0 0.0 0.0 1.0 0.4 0.4
#> ASV_25 0.6 0.6 0.6 0.4 0.6 0.6 1.0 0.4 1.0 0.0
#> ASV_124 0.4 0.4 0.6 0.0 0.4 0.6 0.4 0.4 0.0 1.0
#> ASV_119 0.0 0.0 0.2 0.0 0.0 0.2 0.6 0.0 0.4 0.0
#> ASV_153 0.4 0.4 0.2 0.0 0.4 0.6 0.0 0.6 0.0 0.0
#> ASV_52 0.2 0.2 0.4 0.0 0.0 0.4 0.2 0.0 0.4 0.2
#> ASV_21 0.4 0.0 0.0 0.2 0.0 0.0 0.0 0.0 0.4 0.6
#> ASV_49 0.8 0.6 0.6 0.0 0.0 0.2 0.6 0.2 0.0 0.0
#> ASV_53 0.8 0.4 0.4 0.6 0.0 0.2 0.6 0.2 0.0 0.4
#> ASV_138 0.2 0.8 1.0 0.8 0.4 0.6 0.2 0.2 0.0 1.0
#> ASV_16 0.0 1.0 0.8 0.8 0.2 0.4 0.2 0.0 0.8 0.8
#> ASV_95 0.4 1.0 1.0 0.4 0.2 0.2 0.2 0.2 0.2 0.2
#> ASV_9 0.6 0.8 0.8 0.4 0.0 0.0 0.0 0.0 0.6 0.2
#> ASV_135 0.2 0.2 0.2 0.8 0.2 0.0 0.2 0.2 0.6 0.8
#> ASV_31 0.2 0.4 0.6 0.6 0.0 0.0 0.0 0.0 0.4 0.4
#> ASV_91 0.2 0.2 0.6 0.0 0.6 0.6 0.4 0.4 0.8 0.8
#> ASV_67 0.0 0.2 0.0 0.8 0.0 0.4 0.8 0.6 1.0 0.0
#> ASV_43 0.0 0.2 0.0 0.6 0.6 0.4 0.2 0.6 0.8 0.6
#> ASV_172 0.0 0.0 0.6 0.8 0.2 0.6 0.4 1.0 0.8 0.0
#> ASV_51 0.8 0.8 0.8 0.4 0.6 0.0 0.6 0.6 0.4 1.0
#> ASV_117 0.0 0.4 1.0 0.6 0.2 0.0 0.2 0.6 0.6 0.8
#> ASV_8 0.0 0.0 0.2 0.4 0.0 0.4 0.6 0.0 0.2 0.2
#> ASV_106 0.0 0.2 0.4 0.8 0.0 0.6 0.4 0.2 1.0 0.6
#> ASV_125 0.2 0.4 1.0 0.2 0.0 0.6 0.4 0.2 0.8 0.8
#> ASV_113 0.8 0.6 0.8 0.4 0.4 0.6 0.6 0.2 0.4 0.0
#> ASV_109 0.0 0.2 0.0 0.6 0.0 0.6 0.4 0.6 0.4 0.2
#> ASV_26 0.2 0.8 0.0 0.2 0.0 0.8 0.4 1.0 0.8 0.2
#> ASV_154 0.8 0.4 0.6 0.0 0.0 1.0 0.8 1.0 0.6 0.8
#> ASV_88 1.0 0.0 0.8 0.0 1.0 0.6 0.6 1.0 0.8 0.4
#> ASV_151 0.0 1.0 0.6 0.0 0.8 1.0 0.6 0.4 0.4 0.8
#> ASV_27 0.6 0.4 0.2 0.2 0.8 1.0 0.0 0.6 0.0 0.0
#> ASV_148 0.6 0.2 0.0 0.4 0.8 0.6 0.2 1.0 0.2 0.0
#> ASV_162 0.4 0.8 0.4 0.2 0.0 0.6 0.0 1.0 0.6 0.4
#> ASV_142 0.4 0.0 0.0 0.0 0.6 0.6 1.0 0.4 0.0 0.0
#> ASV_121 0.0 0.0 0.0 0.2 0.2 0.6 0.6 0.4 0.2 0.4
#> ASV_126 0.2 0.6 0.2 0.2 0.6 0.2 0.2 0.6 0.0 0.6
#> ASV_78 0.2 0.8 0.8 0.2 0.6 0.6 0.6 0.6 0.0 0.6
#> ASV_19 0.6 0.4 0.8 0.8 0.4 0.2 0.8 0.2 0.4 0.6
#> ASV_84 0.8 0.6 0.6 1.0 0.8 1.0 0.8 0.8 0.0 0.8
#> ASV_119 ASV_153 ASV_52 ASV_21 ASV_49 ASV_53 ASV_138 ASV_16 ASV_95 ASV_9
#> ASV_102 0.4 0.8 0.6 0.4 0.6 0.8 0.0 0.0 0.2 0.2
#> ASV_136 0.8 0.4 0.8 0.8 0.6 0.6 0.0 0.8 0.0 0.0
#> ASV_75 0.6 0.8 0.8 0.2 0.6 0.8 0.0 0.8 0.0 0.4
#> ASV_110 0.8 0.6 0.6 0.8 0.0 0.8 0.4 0.6 0.6 0.4
#> ASV_3 0.2 0.4 1.0 0.8 0.2 0.2 1.0 0.0 0.6 0.6
#> ASV_55 0.0 0.0 0.6 0.8 0.6 0.6 0.4 0.2 0.2 0.6
#> ASV_116 0.2 0.0 0.8 0.6 0.8 0.2 0.6 0.8 0.6 0.8
#> ASV_40 1.0 0.2 1.0 0.2 0.6 0.8 0.8 0.8 0.6 1.0
#> ASV_4 0.8 0.0 0.2 0.2 0.2 0.0 0.8 0.6 0.4 0.2
#> ASV_144 1.0 0.0 0.0 0.0 0.0 0.0 0.8 0.6 0.0 0.0
#> ASV_11 0.8 0.8 0.6 0.8 1.0 0.6 0.6 0.0 0.4 0.0
#> ASV_61 0.8 0.6 1.0 0.8 0.2 0.4 0.8 0.0 1.0 0.0
#> ASV_94 0.4 0.4 1.0 0.2 0.2 0.8 0.2 0.6 0.4 0.0
#> ASV_57 0.4 0.8 1.0 0.6 0.8 1.0 0.6 0.4 0.8 0.0
#> ASV_129 0.6 0.6 1.0 0.4 0.4 0.8 0.8 0.8 0.2 0.6
#> ASV_93 0.0 0.2 0.8 0.4 0.2 1.0 0.0 0.0 0.2 0.4
#> ASV_7 0.6 0.6 0.4 0.8 0.0 1.0 0.2 0.2 0.0 0.8
#> ASV_13 1.0 0.4 0.4 0.8 0.0 0.8 0.4 0.4 0.4 0.4
#> ASV_12 0.0 0.2 0.2 0.0 0.6 0.2 0.0 0.0 0.0 0.6
#> ASV_105 0.0 0.0 0.0 0.2 0.2 0.4 0.0 0.0 0.4 0.0
#> ASV_71 0.6 0.2 0.0 0.8 0.8 0.6 0.0 0.2 0.2 0.0
#> ASV_48 0.0 1.0 0.0 0.6 0.6 0.4 0.2 1.0 0.0 0.6
#> ASV_90 0.6 0.4 0.4 0.0 0.2 0.4 0.2 0.6 0.8 0.6
#> ASV_108 0.6 0.4 0.0 0.0 0.0 0.0 0.0 0.8 0.0 0.0
#> ASV_100 0.2 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_80 0.2 0.4 0.0 0.6 0.0 0.0 0.0 0.2 0.0 0.0
#> ASV_127 0.4 0.0 0.4 0.0 1.0 0.4 0.0 0.4 0.2 0.6
#> ASV_20 0.4 0.0 0.2 0.0 0.8 0.8 1.0 0.2 0.2 0.0
#> ASV_69 0.6 0.6 0.2 0.4 0.4 0.6 0.4 0.4 0.4 0.8
#> ASV_28 0.6 0.2 0.8 0.0 0.6 0.4 0.6 0.4 0.2 0.0
#> ASV_92 0.4 0.4 0.2 0.4 0.8 0.0 0.4 0.8 0.6 0.2
#> ASV_150 0.6 0.4 0.6 0.0 0.2 0.6 0.8 0.2 0.8 0.2
#> ASV_72 0.0 0.0 0.6 0.2 0.4 0.6 0.2 0.6 0.6 0.2
#> ASV_76 0.4 0.0 0.4 0.4 0.4 1.0 0.2 1.0 0.2 0.0
#> ASV_30 0.0 0.0 0.2 0.0 0.0 0.0 0.2 0.4 0.6 0.0
#> ASV_83 0.0 0.0 0.0 0.2 0.4 0.0 0.4 0.8 0.8 0.2
#> ASV_50 0.0 0.4 0.4 0.0 0.0 0.0 0.6 0.4 0.6 0.6
#> ASV_99 0.2 0.8 0.8 0.2 0.0 0.2 0.2 0.0 0.8 0.2
#> ASV_36 0.2 0.0 0.4 0.0 0.0 0.0 0.4 0.2 0.4 0.4
#> ASV_38 0.8 0.4 0.6 0.2 0.0 0.0 0.6 0.2 0.8 0.6
#> ASV_35 0.2 0.8 0.4 0.4 0.6 0.6 0.0 0.0 0.0 0.6
#> ASV_81 0.4 0.6 0.8 0.0 0.0 0.2 0.2 0.0 0.4 1.0
#> ASV_114 0.2 0.4 0.8 0.4 0.6 0.4 0.8 0.8 0.6 0.0
#> ASV_14 0.8 1.0 0.6 0.8 0.6 0.4 0.2 0.6 1.0 0.0
#> ASV_166 0.2 0.0 0.4 0.8 0.2 0.8 0.2 0.2 0.2 0.4
#> ASV_123 0.0 0.0 0.2 0.2 0.2 0.0 0.0 0.0 0.0 0.6
#> ASV_104 0.0 0.4 0.0 0.4 0.2 0.0 0.4 0.4 0.8 0.2
#> ASV_85 0.0 0.0 0.8 0.8 0.0 0.6 0.2 0.4 0.2 0.0
#> ASV_98 0.0 0.2 0.0 0.2 0.6 0.0 0.2 0.4 0.2 0.6
#> ASV_134 0.0 0.4 0.6 0.8 0.8 0.2 0.0 0.2 0.0 0.4
#> ASV_64 0.2 0.6 0.6 0.8 0.2 0.2 1.0 0.8 0.2 0.6
#> ASV_141 0.0 0.2 0.0 0.6 0.2 0.4 1.0 1.0 0.8 0.4
#> ASV_56 0.0 0.4 0.0 0.0 0.0 0.0 0.6 0.0 0.6 0.0
#> ASV_60 0.0 0.2 0.0 0.0 0.4 0.0 0.8 0.6 1.0 0.2
#> ASV_2 0.0 0.2 0.0 0.0 0.0 0.0 0.8 0.6 0.6 0.2
#> ASV_6 0.0 0.4 0.0 0.0 0.4 0.2 0.8 0.4 1.0 0.2
#> ASV_39 0.0 0.4 0.0 0.2 0.2 0.0 0.8 0.6 0.4 0.2
#> ASV_44 0.2 0.4 0.4 0.2 0.0 0.0 0.2 0.6 0.2 0.6
#> ASV_10 0.2 0.4 0.4 0.2 0.0 0.0 0.8 0.6 0.8 0.2
#> ASV_62 0.2 0.6 0.2 0.2 0.4 0.0 0.4 0.8 0.2 0.2
#> ASV_96 0.2 0.6 0.2 0.0 0.2 0.6 0.4 0.4 0.4 0.0
#> ASV_24 0.6 0.8 0.4 0.0 0.4 0.2 0.4 0.4 0.2 0.0
#> ASV_47 0.0 0.2 0.0 0.0 0.8 0.8 0.8 0.4 0.4 0.0
#> ASV_63 0.0 0.4 0.0 0.0 0.2 0.0 0.4 0.2 0.8 0.0
#> ASV_79 0.0 0.2 0.0 0.2 0.4 0.2 0.2 0.2 0.2 0.0
#> ASV_17 0.0 0.2 0.0 0.0 0.2 0.0 0.8 0.6 0.8 0.2
#> ASV_41 0.0 0.2 0.0 0.0 0.4 0.0 0.8 0.2 0.4 0.2
#> ASV_73 0.0 0.2 0.0 0.0 0.4 0.0 1.0 0.6 0.4 0.2
#> ASV_120 0.0 1.0 0.6 0.4 0.8 0.4 1.0 0.2 0.2 0.0
#> ASV_137 0.6 0.8 0.4 0.8 0.4 0.0 0.4 0.6 1.0 0.2
#> ASV_130 0.2 0.4 0.8 0.8 0.4 0.6 0.8 0.2 0.6 1.0
#> ASV_107 0.4 0.8 0.4 0.2 0.4 0.8 0.4 0.0 0.2 0.0
#> ASV_115 0.4 0.8 0.2 0.6 1.0 0.8 0.2 0.0 0.4 0.4
#> ASV_118 0.0 0.6 0.0 0.6 0.0 0.0 0.2 0.6 0.2 0.8
#> ASV_70 0.4 0.4 0.0 0.0 0.2 0.0 0.4 0.2 0.2 0.0
#> ASV_103 0.0 0.0 0.0 0.8 0.0 0.0 0.4 0.8 0.8 0.8
#> ASV_133 0.2 0.2 0.2 0.0 0.6 0.0 0.8 0.0 0.6 0.8
#> ASV_23 0.8 0.4 0.4 0.0 0.0 0.4 0.8 0.8 0.6 1.0
#> ASV_42 0.2 0.0 0.2 1.0 0.4 0.0 0.8 0.4 0.6 0.2
#> ASV_45 0.8 0.0 0.2 0.0 0.0 0.0 0.4 0.6 0.6 0.6
#> ASV_140 0.2 0.6 0.0 0.6 0.2 0.4 0.2 0.6 0.4 0.0
#> ASV_97 0.6 0.6 0.2 0.2 0.4 0.0 0.8 1.0 0.2 0.2
#> ASV_29 0.8 0.6 0.4 0.8 0.4 0.6 0.6 0.4 0.2 0.8
#> ASV_147 1.0 1.0 0.8 0.0 0.6 0.6 0.8 0.4 0.8 0.2
#> ASV_54 0.0 0.2 0.2 0.6 1.0 0.8 0.6 0.2 1.0 0.6
#> ASV_15 0.6 0.2 1.0 0.2 0.0 0.2 0.4 0.6 0.2 0.8
#> ASV_156 0.0 0.8 0.4 0.8 0.8 0.6 0.4 0.2 0.2 0.2
#> ASV_132 1.0 1.0 0.8 0.8 0.8 0.0 0.0 0.0 0.0 0.8
#> ASV_22 0.6 0.6 0.2 0.0 0.4 0.6 0.4 0.2 0.2 0.0
#> ASV_68 0.6 0.4 0.4 0.2 0.6 0.8 1.0 0.2 1.0 0.2
#> ASV_59 1.0 0.8 0.8 1.0 0.4 0.4 0.0 0.0 0.2 0.2
#> ASV_87 0.4 0.4 0.8 0.2 0.8 1.0 0.4 0.8 0.4 0.8
#> ASV_74 0.6 0.4 0.2 0.4 0.2 0.6 0.0 0.0 0.0 0.4
#> ASV_65 0.8 0.6 0.4 0.2 0.0 0.4 0.0 0.0 0.0 1.0
#> ASV_66 0.2 0.4 0.2 0.2 0.2 0.2 0.6 0.6 0.6 0.2
#> ASV_101 0.4 0.8 0.6 0.8 0.2 0.6 0.2 0.2 0.6 0.0
#> ASV_122 0.2 0.2 0.0 0.0 0.0 0.0 0.0 0.2 0.6 0.0
#> ASV_163 0.0 0.6 0.0 0.4 0.2 0.4 0.8 0.6 0.8 0.2
#> ASV_32 0.2 0.4 0.2 1.0 0.2 0.4 0.6 0.2 0.6 0.6
#> ASV_128 0.0 0.2 0.0 0.2 0.0 0.4 0.8 0.8 0.6 0.6
#> ASV_33 0.0 0.2 0.2 0.2 0.0 0.4 0.8 0.8 0.8 0.4
#> ASV_77 0.0 0.2 0.2 0.4 0.2 0.4 0.8 0.6 0.6 0.8
#> ASV_159 0.4 0.6 0.2 0.2 0.4 0.4 0.4 0.4 0.4 0.0
#> ASV_46 0.0 0.4 0.0 0.4 1.0 0.4 0.8 0.4 0.8 0.4
#> ASV_112 0.0 0.4 0.2 0.4 0.8 0.8 0.2 0.0 0.4 0.6
#> ASV_89 0.0 0.4 0.2 0.0 0.6 0.4 0.8 1.0 1.0 0.8
#> ASV_111 0.2 0.2 0.4 0.0 0.6 0.4 1.0 0.8 1.0 0.8
#> ASV_37 0.0 0.0 0.0 0.2 0.0 0.6 0.8 0.8 0.4 0.4
#> ASV_1 0.0 0.4 0.0 0.0 0.0 0.0 0.4 0.2 0.2 0.0
#> ASV_86 0.2 0.6 0.4 0.0 0.2 0.2 0.6 0.4 0.2 0.0
#> ASV_18 0.6 0.0 0.2 0.0 0.6 0.6 0.2 0.2 0.2 0.0
#> ASV_34 0.0 0.6 0.0 0.0 0.2 0.2 0.2 0.0 0.2 0.0
#> ASV_25 0.4 0.0 0.4 0.4 0.0 0.0 0.0 0.8 0.2 0.6
#> ASV_124 0.0 0.0 0.2 0.6 0.0 0.4 1.0 0.8 0.2 0.2
#> ASV_119 1.0 0.0 0.0 0.0 0.0 0.0 0.6 0.0 0.0 0.6
#> ASV_153 0.0 1.0 0.0 0.2 0.0 0.0 1.0 0.6 0.8 0.8
#> ASV_52 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2
#> ASV_21 0.0 0.2 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_49 0.0 0.0 0.0 0.0 1.0 0.0 0.6 0.2 0.0 0.0
#> ASV_53 0.0 0.0 0.0 0.0 0.0 1.0 0.2 0.2 0.2 0.0
#> ASV_138 0.6 1.0 0.0 0.0 0.6 0.2 1.0 0.0 0.0 0.0
#> ASV_16 0.0 0.6 0.0 0.0 0.2 0.2 0.0 1.0 0.0 0.2
#> ASV_95 0.0 0.8 0.0 0.0 0.0 0.2 0.0 0.0 1.0 0.2
#> ASV_9 0.6 0.8 0.2 0.0 0.0 0.0 0.0 0.2 0.2 1.0
#> ASV_135 0.4 0.8 0.0 0.2 0.2 0.2 0.4 0.6 0.0 0.0
#> ASV_31 0.2 0.8 0.2 0.0 0.6 0.2 0.0 0.0 0.0 0.0
#> ASV_91 0.2 0.2 0.8 0.2 0.8 0.0 0.8 0.4 0.8 0.2
#> ASV_67 0.6 0.6 0.4 1.0 0.6 0.2 0.0 0.6 0.0 0.6
#> ASV_43 0.4 0.2 0.6 0.0 0.6 0.8 0.4 0.0 0.0 0.4
#> ASV_172 0.8 0.6 0.4 0.4 0.8 0.8 0.2 0.0 0.2 0.2
#> ASV_51 0.6 0.6 0.4 0.6 0.6 0.6 0.0 0.0 0.0 0.6
#> ASV_117 0.6 0.2 0.2 0.2 0.4 0.4 0.4 0.2 1.0 1.0
#> ASV_8 0.0 0.0 0.0 0.2 0.2 0.0 0.0 0.0 0.2 0.0
#> ASV_106 0.2 0.6 0.4 0.6 0.4 1.0 0.8 0.2 0.8 0.4
#> ASV_125 0.8 0.0 0.0 0.8 0.8 0.0 0.8 0.4 1.0 0.6
#> ASV_113 0.4 0.6 0.2 0.6 0.6 0.0 1.0 0.6 0.6 0.2
#> ASV_109 0.2 0.6 0.0 0.8 0.0 0.0 1.0 0.2 1.0 0.8
#> ASV_26 0.8 0.0 0.2 0.8 0.0 0.0 0.2 0.6 0.8 0.6
#> ASV_154 0.4 0.6 0.4 0.8 0.0 0.6 0.2 0.2 0.2 0.6
#> ASV_88 0.8 0.6 0.6 0.6 0.4 0.2 0.4 0.0 0.0 0.8
#> ASV_151 1.0 0.6 0.6 0.4 0.2 0.4 0.2 0.0 0.0 0.8
#> ASV_27 0.2 0.0 0.4 0.8 0.0 0.0 0.4 0.2 0.2 0.4
#> ASV_148 0.2 0.0 0.4 1.0 0.0 0.2 0.2 0.2 0.4 0.6
#> ASV_162 0.6 0.6 0.8 0.4 1.0 0.8 0.8 0.6 0.6 1.0
#> ASV_142 0.2 0.0 0.2 0.8 0.6 0.0 0.8 0.6 1.0 0.6
#> ASV_121 0.0 0.8 0.4 0.6 0.2 0.4 0.8 0.6 0.8 0.6
#> ASV_126 0.4 0.4 0.4 0.4 0.0 0.0 0.2 0.4 0.2 0.4
#> ASV_78 0.8 0.2 0.0 0.8 0.2 0.0 0.2 0.4 0.4 0.6
#> ASV_19 0.6 1.0 0.8 0.4 0.6 0.2 0.4 0.2 0.8 0.4
#> ASV_84 1.0 0.6 0.6 0.6 0.2 0.2 0.0 0.0 0.8 0.6
#> ASV_135 ASV_31 ASV_91 ASV_67 ASV_43 ASV_172 ASV_51 ASV_117 ASV_8
#> ASV_102 0.2 0.2 0.0 0.6 0.6 0.4 0.0 0.0 0.2
#> ASV_136 0.0 0.2 0.4 0.6 0.8 0.6 0.4 0.2 0.8
#> ASV_75 0.4 0.2 0.0 0.8 0.8 1.0 0.2 0.2 0.8
#> ASV_110 0.2 0.4 0.2 1.0 0.0 0.0 0.0 0.0 0.6
#> ASV_3 0.2 0.6 0.0 0.2 0.0 0.0 0.4 0.2 0.2
#> ASV_55 1.0 0.4 0.0 0.8 0.0 0.0 0.6 0.2 0.0
#> ASV_116 0.4 0.4 0.0 0.6 0.4 0.2 1.0 0.0 0.0
#> ASV_40 0.4 0.0 0.6 0.0 0.4 0.4 0.0 0.4 0.2
#> ASV_4 0.2 0.4 0.2 0.8 0.0 0.2 0.2 0.2 0.2
#> ASV_144 0.4 0.2 0.8 0.0 0.0 0.0 0.6 0.0 0.6
#> ASV_11 0.4 0.4 0.0 0.4 0.2 0.2 1.0 0.8 0.0
#> ASV_61 0.4 0.4 0.0 0.6 0.2 0.2 0.6 1.0 0.4
#> ASV_94 0.0 0.4 0.0 0.4 0.4 0.4 0.8 0.8 0.2
#> ASV_57 0.4 0.2 0.0 0.6 0.2 0.0 0.8 0.2 0.0
#> ASV_129 0.2 0.6 0.0 0.0 0.0 0.0 0.4 0.2 0.0
#> ASV_93 1.0 0.4 0.0 0.0 0.0 0.0 0.0 0.4 0.8
#> ASV_7 0.6 0.4 0.0 0.8 0.0 0.2 0.0 0.2 1.0
#> ASV_13 0.8 0.4 0.0 0.6 0.2 0.2 0.6 0.4 0.4
#> ASV_12 0.6 0.0 0.6 0.0 0.2 0.6 0.4 0.4 0.0
#> ASV_105 0.0 0.0 0.2 0.2 0.4 0.4 0.2 0.2 0.0
#> ASV_71 0.2 0.0 0.0 0.0 0.0 0.0 0.6 0.2 0.0
#> ASV_48 0.2 0.4 0.2 0.2 0.4 0.4 0.4 0.6 0.2
#> ASV_90 0.2 0.8 0.2 0.0 1.0 0.4 0.8 0.6 0.0
#> ASV_108 0.0 0.0 0.2 0.0 0.4 0.2 0.0 0.0 0.8
#> ASV_100 0.0 0.0 0.2 0.0 0.6 0.8 0.2 0.6 0.2
#> ASV_80 0.2 0.6 0.2 0.0 0.2 0.2 0.2 0.0 0.0
#> ASV_127 1.0 0.0 0.8 0.0 0.2 0.0 0.2 0.0 0.2
#> ASV_20 0.8 0.2 0.4 0.4 0.2 0.4 0.8 0.2 0.2
#> ASV_69 0.0 0.0 0.0 0.6 0.6 0.8 0.0 0.4 0.4
#> ASV_28 0.2 0.0 0.6 0.4 0.6 0.6 0.0 0.4 0.8
#> ASV_92 0.2 0.0 0.2 0.2 0.6 0.8 0.0 0.8 0.0
#> ASV_150 0.0 0.0 0.0 0.0 0.0 0.0 0.4 0.2 1.0
#> ASV_72 0.8 0.4 0.2 0.2 1.0 1.0 0.2 0.6 0.2
#> ASV_76 0.0 0.0 0.2 0.6 0.6 0.6 0.6 0.4 0.0
#> ASV_30 0.0 1.0 0.8 1.0 0.8 0.8 0.2 0.0 0.4
#> ASV_83 0.0 0.2 0.8 0.2 0.0 0.0 0.2 0.4 0.6
#> ASV_50 0.0 0.6 0.2 0.0 0.0 0.0 0.4 1.0 0.6
#> ASV_99 0.8 0.0 0.2 0.4 0.8 0.6 0.4 0.0 0.0
#> ASV_36 0.8 0.4 0.8 0.4 0.6 0.6 0.2 0.2 0.2
#> ASV_38 0.8 0.4 0.4 0.6 0.6 0.6 0.4 0.6 0.0
#> ASV_35 0.2 0.6 0.0 0.2 0.0 0.0 0.6 0.8 1.0
#> ASV_81 0.0 0.6 0.2 0.6 0.6 0.2 0.2 0.0 0.0
#> ASV_114 0.0 0.0 0.0 0.0 0.0 0.0 0.6 0.0 0.0
#> ASV_14 0.0 0.2 0.2 0.0 0.0 0.0 0.4 0.2 0.8
#> ASV_166 0.0 0.4 0.0 0.0 0.0 0.0 0.4 1.0 0.6
#> ASV_123 0.0 0.0 0.4 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_104 0.0 0.2 0.2 0.6 0.0 0.2 0.6 0.2 0.0
#> ASV_85 0.4 0.8 1.0 0.4 0.4 0.8 0.0 0.0 0.4
#> ASV_98 0.2 1.0 1.0 0.8 0.0 0.0 0.8 0.0 0.0
#> ASV_134 0.0 0.4 0.6 0.6 0.0 0.0 0.8 0.0 0.4
#> ASV_64 0.0 0.8 0.8 0.0 0.0 0.0 0.8 0.0 0.2
#> ASV_141 0.2 0.2 0.2 0.0 0.0 0.0 0.0 0.6 0.0
#> ASV_56 0.2 0.2 0.4 0.2 0.0 0.0 0.8 0.2 0.0
#> ASV_60 0.2 0.2 0.2 0.2 0.0 0.0 0.6 0.0 0.0
#> ASV_2 0.2 0.4 0.6 0.4 0.0 0.0 0.2 0.4 0.2
#> ASV_6 0.2 0.2 0.2 0.0 0.0 0.0 0.6 0.8 0.0
#> ASV_39 0.0 0.2 0.8 0.2 0.0 0.0 0.8 0.2 0.0
#> ASV_44 0.2 0.4 0.2 0.2 0.0 0.0 0.6 0.4 0.2
#> ASV_10 0.2 0.2 0.4 0.0 0.0 0.0 0.8 0.0 0.0
#> ASV_62 0.0 0.2 0.4 0.8 0.0 0.0 0.4 0.0 0.0
#> ASV_96 0.0 0.0 1.0 0.4 0.0 0.0 0.8 0.2 0.0
#> ASV_24 0.0 0.0 0.8 0.0 0.0 0.0 0.8 0.4 0.4
#> ASV_47 0.0 0.0 0.6 0.2 0.0 0.2 0.6 0.8 0.0
#> ASV_63 0.2 0.0 0.8 0.0 0.0 0.0 0.6 0.0 0.0
#> ASV_79 0.0 0.0 0.6 0.0 0.0 0.0 0.8 0.0 0.0
#> ASV_17 0.2 0.2 0.2 0.0 0.0 0.0 0.2 0.2 0.0
#> ASV_41 0.0 0.2 0.8 0.0 0.0 0.0 0.6 0.0 0.0
#> ASV_73 0.0 0.2 0.8 0.0 0.0 0.0 0.8 0.0 0.0
#> ASV_120 0.0 0.2 0.8 0.0 0.4 0.0 1.0 0.6 0.0
#> ASV_137 0.0 0.2 0.4 0.0 0.0 0.0 0.6 0.4 0.0
#> ASV_130 0.6 0.6 0.6 0.2 0.0 0.4 0.2 0.2 0.6
#> ASV_107 0.0 0.0 0.6 0.4 0.6 1.0 0.4 0.0 0.0
#> ASV_115 0.4 0.4 0.6 0.6 0.2 0.2 0.0 0.0 0.0
#> ASV_118 0.8 0.8 0.0 0.0 0.0 0.0 0.0 0.2 0.8
#> ASV_70 0.6 0.4 0.8 0.8 0.2 0.2 0.4 0.6 0.8
#> ASV_103 0.4 0.6 0.6 0.2 0.6 0.8 0.0 0.0 0.2
#> ASV_133 1.0 0.2 0.6 0.8 0.2 0.0 0.8 0.6 0.8
#> ASV_23 0.0 0.0 0.6 0.0 0.0 0.0 0.6 1.0 0.6
#> ASV_42 0.6 0.0 0.2 0.0 0.6 0.6 0.8 0.4 0.8
#> ASV_45 0.2 0.0 0.8 0.0 0.0 0.0 0.6 0.6 0.8
#> ASV_140 0.0 0.2 0.6 0.0 0.2 0.0 0.6 0.0 0.0
#> ASV_97 0.0 0.4 0.2 1.0 0.0 0.0 1.0 0.4 0.2
#> ASV_29 0.0 0.4 0.0 0.4 0.0 0.0 0.4 0.2 0.0
#> ASV_147 0.8 0.8 0.0 0.6 0.4 0.8 0.0 0.0 0.2
#> ASV_54 0.4 0.2 0.4 0.2 0.2 0.2 1.0 0.0 0.0
#> ASV_15 0.0 0.0 0.4 0.8 0.6 0.8 0.4 0.0 0.0
#> ASV_156 0.0 0.0 1.0 1.0 0.4 0.4 0.4 0.0 0.0
#> ASV_132 0.4 0.2 0.2 0.0 0.0 0.0 0.2 0.4 0.4
#> ASV_22 0.0 0.0 0.4 0.6 0.6 0.6 0.6 0.2 0.0
#> ASV_68 0.4 0.0 0.2 0.4 0.6 0.6 0.0 0.4 0.0
#> ASV_59 0.4 1.0 0.2 0.0 0.0 0.0 0.2 1.0 0.2
#> ASV_87 0.2 0.6 0.2 0.8 0.6 0.0 0.4 1.0 0.0
#> ASV_74 0.0 0.2 0.4 0.4 0.4 0.4 0.2 0.0 0.0
#> ASV_65 0.8 0.6 0.4 0.4 0.8 1.0 0.0 0.0 0.0
#> ASV_66 0.0 0.4 0.6 0.0 0.0 0.0 0.8 0.2 0.0
#> ASV_101 0.0 0.6 0.2 0.0 0.0 0.0 0.8 0.4 0.0
#> ASV_122 0.0 0.2 0.0 0.4 0.0 0.0 0.2 0.0 0.0
#> ASV_163 0.0 0.4 0.6 0.0 0.0 0.0 0.6 0.4 0.0
#> ASV_32 0.4 0.8 0.6 0.6 0.0 0.0 1.0 0.4 0.4
#> ASV_128 0.2 0.4 0.4 0.6 0.2 0.2 0.6 0.8 0.4
#> ASV_33 0.2 0.4 0.8 0.8 0.0 0.0 1.0 1.0 0.2
#> ASV_77 0.4 0.4 0.4 0.4 0.2 0.0 0.8 0.6 0.2
#> ASV_159 0.2 0.0 0.4 0.6 0.4 0.4 0.4 0.0 0.0
#> ASV_46 0.4 0.2 0.2 0.6 0.0 0.2 0.6 0.2 0.0
#> ASV_112 0.2 0.2 0.2 0.0 0.0 0.0 0.8 0.0 0.0
#> ASV_89 0.2 0.4 0.2 0.2 0.2 0.0 0.8 0.4 0.0
#> ASV_111 0.2 0.6 0.6 0.0 0.0 0.6 0.8 1.0 0.2
#> ASV_37 0.8 0.6 0.0 0.8 0.6 0.8 0.4 0.6 0.4
#> ASV_1 0.2 0.0 0.6 0.0 0.6 0.2 0.6 0.2 0.0
#> ASV_86 0.0 0.0 0.6 0.4 0.4 0.6 0.0 0.0 0.4
#> ASV_18 0.2 0.0 0.4 0.8 0.2 0.4 0.6 0.2 0.6
#> ASV_34 0.2 0.0 0.4 0.6 0.6 1.0 0.6 0.6 0.0
#> ASV_25 0.6 0.4 0.8 1.0 0.8 0.8 0.4 0.6 0.2
#> ASV_124 0.8 0.4 0.8 0.0 0.6 0.0 1.0 0.8 0.2
#> ASV_119 0.4 0.2 0.2 0.6 0.4 0.8 0.6 0.6 0.0
#> ASV_153 0.8 0.8 0.2 0.6 0.2 0.6 0.6 0.2 0.0
#> ASV_52 0.0 0.2 0.8 0.4 0.6 0.4 0.4 0.2 0.0
#> ASV_21 0.2 0.0 0.2 1.0 0.0 0.4 0.6 0.2 0.2
#> ASV_49 0.2 0.6 0.8 0.6 0.6 0.8 0.6 0.4 0.2
#> ASV_53 0.2 0.2 0.0 0.2 0.8 0.8 0.6 0.4 0.0
#> ASV_138 0.4 0.0 0.8 0.0 0.4 0.2 0.0 0.4 0.0
#> ASV_16 0.6 0.0 0.4 0.6 0.0 0.0 0.0 0.2 0.0
#> ASV_95 0.0 0.0 0.8 0.0 0.0 0.2 0.0 1.0 0.2
#> ASV_9 0.0 0.0 0.2 0.6 0.4 0.2 0.6 1.0 0.0
#> ASV_135 1.0 0.0 0.4 0.6 0.0 0.0 0.6 0.0 0.4
#> ASV_31 0.0 1.0 0.0 0.2 0.4 0.2 0.4 0.2 0.2
#> ASV_91 0.4 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_67 0.6 0.2 0.0 1.0 0.0 0.0 0.4 0.8 0.4
#> ASV_43 0.0 0.4 0.0 0.0 1.0 0.0 0.6 0.2 0.2
#> ASV_172 0.0 0.2 0.0 0.0 0.0 1.0 0.6 0.2 0.4
#> ASV_51 0.6 0.4 0.0 0.4 0.6 0.6 1.0 0.0 0.2
#> ASV_117 0.0 0.2 0.0 0.8 0.2 0.2 0.0 1.0 0.0
#> ASV_8 0.4 0.2 0.0 0.4 0.2 0.4 0.2 0.0 1.0
#> ASV_106 0.2 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_125 0.0 0.8 0.4 0.2 0.4 0.4 0.4 0.2 0.4
#> ASV_113 0.0 0.6 0.2 0.6 0.0 0.0 0.8 1.0 0.8
#> ASV_109 0.0 0.8 0.4 0.8 0.0 0.0 0.2 0.0 0.0
#> ASV_26 0.0 0.8 0.8 0.2 0.0 0.0 0.6 0.0 0.0
#> ASV_154 0.2 0.2 0.2 0.8 0.2 0.2 0.0 1.0 0.8
#> ASV_88 0.0 0.6 0.0 0.6 0.0 0.0 0.2 0.2 0.0
#> ASV_151 0.2 0.6 0.0 0.2 0.0 0.0 0.4 0.2 0.0
#> ASV_27 0.0 0.0 0.2 0.6 0.2 0.2 0.6 0.8 0.6
#> ASV_148 0.2 0.8 0.2 0.6 0.0 0.0 0.4 0.4 0.6
#> ASV_162 0.2 0.4 0.0 0.4 0.2 0.2 0.2 1.0 1.0
#> ASV_142 0.4 0.4 0.6 0.2 0.0 0.0 0.2 1.0 0.2
#> ASV_121 0.2 0.4 0.2 0.0 0.0 0.0 0.4 1.0 0.4
#> ASV_126 0.0 0.2 1.0 0.4 0.0 0.0 0.4 0.8 0.4
#> ASV_78 0.4 0.8 0.2 0.8 0.8 0.8 0.2 0.0 0.0
#> ASV_19 0.0 0.2 1.0 0.4 0.8 0.0 0.6 0.0 0.4
#> ASV_84 0.0 0.8 0.0 0.2 0.0 0.2 0.0 0.0 0.6
#> ASV_106 ASV_125 ASV_113 ASV_109 ASV_26 ASV_154 ASV_88 ASV_151 ASV_27
#> ASV_102 0.0 1.0 0.2 1.0 0.6 0.0 0.2 0.6 0.0
#> ASV_136 0.2 0.0 0.4 0.6 0.0 0.6 0.4 0.6 0.2
#> ASV_75 0.8 0.0 0.0 0.6 0.4 0.2 0.4 0.4 1.0
#> ASV_110 0.4 1.0 0.6 0.0 0.2 0.6 0.0 0.0 0.0
#> ASV_3 0.6 0.4 0.2 0.0 0.2 0.4 0.0 0.0 0.0
#> ASV_55 0.4 0.8 0.8 0.8 0.0 0.4 0.0 0.0 0.0
#> ASV_116 0.4 0.2 0.8 0.0 0.2 0.4 0.0 0.2 0.2
#> ASV_40 0.8 0.8 0.6 0.8 1.0 0.4 0.4 0.2 0.0
#> ASV_4 0.4 0.0 0.0 0.2 0.0 0.2 0.2 1.0 0.0
#> ASV_144 0.0 0.0 0.2 1.0 0.4 0.6 0.2 0.2 0.0
#> ASV_11 0.2 0.8 1.0 0.6 1.0 0.8 0.0 0.4 0.2
#> ASV_61 0.4 0.8 0.4 1.0 0.6 0.4 0.2 0.6 0.8
#> ASV_94 0.0 0.6 0.2 0.4 0.8 0.2 0.0 0.6 0.8
#> ASV_57 0.2 0.8 0.4 0.6 1.0 0.2 0.4 0.6 0.8
#> ASV_129 0.0 0.8 1.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_93 0.2 0.8 1.0 0.2 0.2 0.2 0.0 0.0 0.0
#> ASV_7 0.6 0.6 0.6 0.0 0.4 0.0 0.0 0.0 0.0
#> ASV_13 0.6 1.0 1.0 0.0 0.2 0.2 0.0 0.0 0.0
#> ASV_12 0.0 0.2 0.6 0.8 1.0 0.2 0.2 0.0 0.0
#> ASV_105 0.0 0.4 0.6 0.0 0.0 0.2 0.0 0.0 0.2
#> ASV_71 0.2 0.0 0.0 0.0 0.0 0.8 0.0 0.0 0.0
#> ASV_48 0.8 0.0 0.6 0.0 0.0 0.0 0.0 0.0 0.2
#> ASV_90 0.0 0.8 1.0 0.0 0.6 0.0 0.0 0.0 0.2
#> ASV_108 0.4 0.0 0.0 0.0 0.2 0.2 0.6 0.4 0.0
#> ASV_100 0.4 0.6 0.0 0.8 1.0 0.8 0.4 0.6 0.6
#> ASV_80 0.6 0.0 0.0 0.8 0.6 0.0 0.8 0.6 0.0
#> ASV_127 0.0 1.0 0.6 0.4 0.0 0.0 0.4 0.4 0.4
#> ASV_20 0.4 0.6 0.4 0.4 0.0 0.8 0.2 0.4 0.2
#> ASV_69 0.8 0.4 0.8 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_28 0.8 0.4 0.0 1.0 0.6 0.2 1.0 1.0 0.2
#> ASV_92 0.8 0.0 0.0 0.4 0.6 0.0 0.2 0.6 1.0
#> ASV_150 0.2 0.0 0.8 0.2 0.0 0.8 0.2 0.0 0.2
#> ASV_72 0.4 1.0 0.0 0.6 0.0 1.0 0.6 1.0 0.4
#> ASV_76 0.8 0.2 0.4 0.4 0.4 0.4 0.4 0.8 0.2
#> ASV_30 0.0 0.4 0.2 0.4 0.6 0.0 0.4 0.6 0.0
#> ASV_83 1.0 0.0 0.2 0.0 0.0 0.4 0.8 1.0 0.0
#> ASV_50 0.2 0.0 0.2 0.0 0.4 0.8 0.6 0.4 0.0
#> ASV_99 0.0 0.4 0.2 0.6 0.6 0.2 0.6 0.6 0.0
#> ASV_36 0.2 0.0 0.2 0.0 0.0 0.2 0.4 1.0 0.0
#> ASV_38 0.2 0.2 0.2 0.0 0.0 0.6 0.2 0.6 0.0
#> ASV_35 0.8 0.6 0.4 0.6 0.8 0.0 0.0 0.0 0.0
#> ASV_81 0.0 0.2 0.2 0.2 0.6 0.2 0.2 0.2 0.0
#> ASV_114 0.0 0.2 0.0 1.0 0.4 0.2 0.0 0.0 0.6
#> ASV_14 0.8 0.6 0.0 0.8 0.0 0.6 0.2 0.0 0.8
#> ASV_166 0.8 0.4 0.2 0.0 0.0 0.2 0.0 0.0 0.6
#> ASV_123 0.0 0.0 0.4 0.0 0.2 0.0 0.8 0.6 1.0
#> ASV_104 0.0 0.0 0.8 0.0 0.0 1.0 0.0 0.0 0.4
#> ASV_85 0.2 0.2 0.2 0.0 0.2 0.2 0.8 0.2 0.4
#> ASV_98 0.0 0.0 0.0 0.0 0.0 0.0 0.4 0.0 0.2
#> ASV_134 0.0 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.4
#> ASV_64 0.0 0.2 0.2 0.0 0.0 0.0 0.0 0.0 0.2
#> ASV_141 0.0 0.4 0.6 0.0 0.2 0.8 1.0 0.4 0.2
#> ASV_56 0.0 0.2 0.4 0.0 0.6 0.4 0.2 0.4 0.0
#> ASV_60 0.0 0.0 0.4 0.0 0.0 0.8 0.4 0.2 0.2
#> ASV_2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.8 0.0
#> ASV_6 0.0 0.0 0.0 0.0 0.6 0.6 0.8 0.8 0.4
#> ASV_39 0.0 0.0 0.0 0.0 0.0 0.2 0.8 0.8 0.0
#> ASV_44 0.0 0.0 0.4 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_10 0.0 0.0 0.0 0.0 0.0 0.2 0.0 0.0 0.0
#> ASV_62 0.0 0.2 0.0 0.0 0.0 0.4 0.0 0.0 0.0
#> ASV_96 0.0 0.6 0.0 0.0 0.2 0.4 0.2 0.4 0.0
#> ASV_24 0.0 1.0 0.6 0.0 0.6 0.6 0.4 0.8 0.8
#> ASV_47 0.0 0.0 0.0 0.0 0.4 0.8 0.8 0.0 0.2
#> ASV_63 0.0 0.2 0.0 0.0 0.8 0.4 1.0 0.8 0.8
#> ASV_79 0.0 0.0 0.2 0.0 0.0 0.6 0.4 0.6 0.8
#> ASV_17 0.0 0.0 0.2 0.0 0.0 0.6 0.6 0.4 0.4
#> ASV_41 0.0 0.0 0.0 0.0 0.0 0.8 0.4 0.4 0.4
#> ASV_73 0.0 0.0 0.0 0.0 0.4 0.2 0.8 1.0 0.6
#> ASV_120 0.2 0.6 0.6 0.0 0.2 0.8 0.6 0.6 0.4
#> ASV_137 0.0 0.0 0.2 0.0 0.8 0.6 0.0 0.0 0.6
#> ASV_130 0.8 0.0 0.0 0.6 0.4 1.0 0.2 0.0 0.0
#> ASV_107 0.2 0.2 0.6 0.4 0.6 0.6 0.6 0.4 0.0
#> ASV_115 0.0 0.2 0.2 0.2 0.6 0.2 0.0 0.2 0.0
#> ASV_118 0.8 0.0 0.2 0.0 0.0 0.2 1.0 0.2 0.4
#> ASV_70 0.8 0.0 0.8 0.6 0.2 0.2 0.6 0.8 0.8
#> ASV_103 0.8 0.0 0.0 0.4 0.2 0.8 0.6 1.0 0.0
#> ASV_133 1.0 0.8 0.4 0.8 0.2 0.8 0.0 0.0 0.8
#> ASV_23 0.4 0.6 0.6 0.8 0.0 0.4 0.8 0.0 0.4
#> ASV_42 1.0 0.0 0.2 0.4 0.4 0.2 0.4 0.6 0.2
#> ASV_45 0.6 0.6 1.0 0.4 0.6 0.2 1.0 1.0 0.0
#> ASV_140 0.0 0.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_97 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2
#> ASV_29 0.0 0.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_147 0.0 0.6 0.6 0.0 0.0 0.4 1.0 0.4 0.8
#> ASV_54 0.0 0.6 0.2 0.4 0.6 0.6 0.6 0.6 0.4
#> ASV_15 0.0 0.2 0.4 0.8 0.2 1.0 0.8 0.0 0.0
#> ASV_156 0.0 0.2 0.8 0.0 0.0 0.8 0.4 0.6 0.0
#> ASV_132 0.2 0.0 0.0 0.0 0.8 0.0 0.0 0.4 0.4
#> ASV_22 0.0 0.0 0.0 0.8 0.8 1.0 0.8 1.0 0.0
#> ASV_68 0.0 0.0 0.0 0.6 0.2 0.0 0.4 0.8 0.0
#> ASV_59 0.0 0.0 0.8 0.0 1.0 0.4 0.0 0.0 0.8
#> ASV_87 0.0 0.6 1.0 0.0 0.0 0.8 0.0 0.0 0.4
#> ASV_74 0.0 0.8 0.8 0.0 0.0 0.2 0.0 0.0 0.8
#> ASV_65 0.0 0.0 0.8 0.4 0.2 0.2 0.4 1.0 0.0
#> ASV_66 0.0 0.0 0.0 0.0 0.0 0.2 0.0 0.0 0.0
#> ASV_101 0.2 1.0 0.0 0.0 0.0 0.6 0.0 0.0 0.0
#> ASV_122 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_163 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.0
#> ASV_32 0.0 0.0 1.0 0.0 0.0 0.8 0.0 0.0 0.0
#> ASV_128 0.6 0.6 0.0 0.0 0.0 0.2 0.8 0.2 0.0
#> ASV_33 0.2 0.4 0.2 0.0 1.0 0.6 0.8 1.0 0.0
#> ASV_77 0.0 0.2 0.6 0.0 0.0 0.0 0.2 0.0 0.2
#> ASV_159 0.0 0.4 0.8 0.0 0.4 0.8 0.8 0.2 0.6
#> ASV_46 0.2 0.0 0.4 0.0 0.0 1.0 0.6 0.2 0.4
#> ASV_112 0.0 0.2 0.8 0.0 0.2 0.8 1.0 0.0 0.6
#> ASV_89 0.2 0.4 0.6 0.2 0.8 0.4 0.0 1.0 0.4
#> ASV_111 0.4 1.0 0.8 0.0 0.0 0.6 0.8 0.6 0.2
#> ASV_37 0.8 0.2 0.4 0.6 0.2 0.0 0.0 0.0 0.2
#> ASV_1 0.0 0.0 0.4 0.0 0.0 0.0 1.0 0.8 0.8
#> ASV_86 0.6 0.6 0.6 0.6 0.8 1.0 0.6 1.0 1.0
#> ASV_18 0.4 0.4 0.6 0.4 0.4 0.8 0.6 0.6 0.0
#> ASV_34 0.2 0.2 0.2 0.6 1.0 1.0 1.0 0.4 0.6
#> ASV_25 1.0 0.8 0.4 0.4 0.8 0.6 0.8 0.4 0.0
#> ASV_124 0.6 0.8 0.0 0.2 0.2 0.8 0.4 0.8 0.0
#> ASV_119 0.2 0.8 0.4 0.2 0.8 0.4 0.8 1.0 0.2
#> ASV_153 0.6 0.0 0.6 0.6 0.0 0.6 0.6 0.6 0.0
#> ASV_52 0.4 0.0 0.2 0.0 0.2 0.4 0.6 0.6 0.4
#> ASV_21 0.6 0.8 0.6 0.8 0.8 0.8 0.6 0.4 0.8
#> ASV_49 0.4 0.8 0.6 0.0 0.0 0.0 0.4 0.2 0.0
#> ASV_53 1.0 0.0 0.0 0.0 0.0 0.6 0.2 0.4 0.0
#> ASV_138 0.8 0.8 1.0 1.0 0.2 0.2 0.4 0.2 0.4
#> ASV_16 0.2 0.4 0.6 0.2 0.6 0.2 0.0 0.0 0.2
#> ASV_95 0.8 1.0 0.6 1.0 0.8 0.2 0.0 0.0 0.2
#> ASV_9 0.4 0.6 0.2 0.8 0.6 0.6 0.8 0.8 0.4
#> ASV_135 0.2 0.0 0.0 0.0 0.0 0.2 0.0 0.2 0.0
#> ASV_31 0.2 0.8 0.6 0.8 0.8 0.2 0.6 0.6 0.0
#> ASV_91 0.0 0.4 0.2 0.4 0.8 0.2 0.0 0.0 0.2
#> ASV_67 0.0 0.2 0.6 0.8 0.2 0.8 0.6 0.2 0.6
#> ASV_43 0.0 0.4 0.0 0.0 0.0 0.2 0.0 0.0 0.2
#> ASV_172 0.0 0.4 0.0 0.0 0.0 0.2 0.0 0.0 0.2
#> ASV_51 0.0 0.4 0.8 0.2 0.6 0.0 0.2 0.4 0.6
#> ASV_117 0.0 0.2 1.0 0.0 0.0 1.0 0.2 0.2 0.8
#> ASV_8 0.0 0.4 0.8 0.0 0.0 0.8 0.0 0.0 0.6
#> ASV_106 1.0 0.8 0.4 0.0 0.0 0.2 0.2 0.0 1.0
#> ASV_125 0.8 1.0 0.0 0.0 0.2 0.0 0.2 0.6 0.0
#> ASV_113 0.4 0.0 1.0 0.0 0.0 0.2 0.6 0.6 0.2
#> ASV_109 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0
#> ASV_26 0.0 0.2 0.0 0.0 1.0 0.0 0.0 0.0 0.0
#> ASV_154 0.2 0.0 0.2 0.0 0.0 1.0 0.0 0.0 0.2
#> ASV_88 0.2 0.2 0.6 0.0 0.0 0.0 1.0 0.0 0.0
#> ASV_151 0.0 0.6 0.6 0.0 0.0 0.0 0.0 1.0 0.0
#> ASV_27 1.0 0.0 0.2 0.0 0.0 0.2 0.0 0.0 1.0
#> ASV_148 0.8 0.2 0.6 0.0 0.0 0.2 0.0 0.0 0.0
#> ASV_162 0.2 0.2 0.0 0.0 0.0 0.6 0.0 0.2 0.0
#> ASV_142 0.4 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.0
#> ASV_121 0.0 0.2 0.6 0.6 0.2 0.2 0.0 0.6 0.0
#> ASV_126 0.0 1.0 0.2 0.0 0.0 0.4 0.4 1.0 0.0
#> ASV_78 0.4 0.4 0.6 0.4 0.6 0.2 0.2 1.0 0.6
#> ASV_19 0.8 0.2 0.2 0.2 0.4 0.4 0.0 0.4 0.0
#> ASV_84 0.0 0.6 0.8 0.0 0.6 0.0 0.0 0.2 0.6
#> ASV_148 ASV_162 ASV_142 ASV_121 ASV_126 ASV_78 ASV_19 ASV_84
#> ASV_102 0.6 0.6 0.4 0.0 0.8 0.2 0.4 0.0
#> ASV_136 0.0 0.0 0.4 0.2 0.6 0.4 0.4 0.2
#> ASV_75 0.4 0.4 0.4 0.4 0.8 0.0 0.8 0.4
#> ASV_110 0.0 0.0 0.0 0.0 0.0 0.8 0.2 0.0
#> ASV_3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.4
#> ASV_55 0.0 0.0 0.2 0.2 1.0 0.0 0.0 0.4
#> ASV_116 0.0 0.2 0.0 0.4 0.2 0.6 0.0 0.8
#> ASV_40 0.0 0.0 0.0 1.0 1.0 0.8 0.4 0.8
#> ASV_4 0.0 0.0 0.0 0.2 0.4 0.6 0.0 0.4
#> ASV_144 0.0 1.0 0.0 0.6 0.6 0.0 0.4 0.6
#> ASV_11 0.0 0.0 0.6 0.0 0.2 0.6 0.6 0.8
#> ASV_61 0.4 0.0 0.6 0.0 0.4 0.0 0.4 0.8
#> ASV_94 0.8 0.0 0.0 0.0 0.6 0.0 0.6 0.8
#> ASV_57 1.0 0.0 0.4 0.0 0.6 0.2 0.8 0.6
#> ASV_129 0.0 0.2 0.8 0.0 0.6 0.0 0.2 0.0
#> ASV_93 0.0 0.2 0.6 0.4 0.4 0.6 0.2 0.0
#> ASV_7 0.0 0.2 0.4 0.4 0.6 0.0 0.2 0.0
#> ASV_13 0.0 0.0 0.0 0.6 0.0 1.0 0.0 0.2
#> ASV_12 0.0 0.6 0.4 0.4 0.4 0.0 0.0 0.2
#> ASV_105 0.0 0.2 0.4 0.2 0.8 0.0 1.0 0.2
#> ASV_71 0.0 0.0 0.0 0.2 0.2 0.0 0.6 0.0
#> ASV_48 0.2 0.8 0.8 0.2 0.6 0.6 0.4 0.0
#> ASV_90 0.4 0.8 0.2 0.8 0.0 0.2 0.6 0.0
#> ASV_108 0.0 0.8 0.4 0.8 0.8 0.8 0.2 0.2
#> ASV_100 0.4 0.4 0.2 0.2 0.6 0.0 0.6 0.8
#> ASV_80 0.8 0.4 0.4 0.4 0.8 0.2 0.2 0.8
#> ASV_127 0.4 0.0 0.0 1.0 0.0 0.4 0.6 0.6
#> ASV_20 0.2 0.0 0.4 0.6 0.0 0.6 0.0 0.4
#> ASV_69 0.0 0.4 0.8 0.8 1.0 0.8 1.0 0.8
#> ASV_28 0.6 0.4 0.6 0.6 0.8 0.8 0.0 0.4
#> ASV_92 0.6 0.8 0.0 0.8 0.4 0.4 0.6 0.4
#> ASV_150 0.4 0.8 0.4 0.4 0.6 0.2 0.4 0.2
#> ASV_72 0.0 0.4 0.6 0.8 1.0 0.4 0.2 1.0
#> ASV_76 0.8 0.4 0.2 0.4 0.4 0.4 0.0 0.4
#> ASV_30 0.0 0.4 0.0 0.0 0.2 0.0 0.0 0.2
#> ASV_83 0.0 0.2 0.0 0.2 0.0 0.0 0.0 0.4
#> ASV_50 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.4
#> ASV_99 0.0 0.2 0.0 0.6 0.0 0.0 0.0 1.0
#> ASV_36 0.0 0.2 0.0 0.2 0.0 0.0 0.0 0.4
#> ASV_38 0.0 0.2 0.0 0.4 0.0 0.0 0.0 0.0
#> ASV_35 0.8 0.4 0.6 0.8 0.6 1.0 0.0 0.2
#> ASV_81 0.0 0.0 0.0 0.6 1.0 0.0 0.0 0.2
#> ASV_114 0.4 0.2 0.2 0.0 0.0 0.2 0.0 0.0
#> ASV_14 0.0 0.0 0.0 0.0 0.0 0.2 0.0 0.4
#> ASV_166 0.2 0.0 0.0 0.0 0.8 0.0 0.0 0.6
#> ASV_123 0.6 1.0 0.0 0.4 0.0 0.2 0.4 0.0
#> ASV_104 0.0 0.6 0.0 0.4 0.0 0.0 0.8 0.4
#> ASV_85 0.4 0.6 0.0 0.8 0.6 0.4 0.2 0.8
#> ASV_98 0.0 0.6 0.0 0.0 0.0 0.4 0.2 0.4
#> ASV_134 0.0 1.0 0.0 0.2 0.0 0.2 0.0 0.0
#> ASV_64 0.0 0.2 0.0 0.0 0.0 1.0 0.0 0.0
#> ASV_141 0.2 1.0 0.0 0.0 0.2 0.0 1.0 1.0
#> ASV_56 0.4 0.4 0.0 0.0 0.2 0.2 1.0 0.4
#> ASV_60 0.4 0.0 0.0 0.0 0.0 1.0 0.0 0.4
#> ASV_2 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.4
#> ASV_6 0.2 0.2 0.0 0.0 0.0 0.0 0.2 0.6
#> ASV_39 0.0 0.0 0.0 0.0 0.0 0.8 0.0 0.2
#> ASV_44 0.0 0.2 0.0 0.0 0.0 0.2 0.4 0.0
#> ASV_10 0.0 0.0 0.0 0.0 0.0 1.0 0.2 0.0
#> ASV_62 0.0 0.0 0.0 0.2 0.0 1.0 0.0 0.0
#> ASV_96 0.0 0.6 0.6 0.2 0.0 0.8 0.2 0.0
#> ASV_24 0.2 0.6 0.0 0.0 0.0 0.4 0.0 0.0
#> ASV_47 0.4 0.4 0.0 0.0 0.0 0.6 0.0 0.4
#> ASV_63 0.2 0.2 0.0 0.0 0.0 0.4 0.2 0.8
#> ASV_79 0.6 0.4 0.2 0.2 0.0 0.0 0.6 0.6
#> ASV_17 0.2 0.0 0.0 0.0 0.0 0.4 0.6 1.0
#> ASV_41 0.2 0.4 0.0 0.2 0.0 0.2 0.2 0.6
#> ASV_73 0.8 1.0 0.2 0.0 0.0 0.8 0.2 0.2
#> ASV_120 0.4 0.6 0.4 0.0 0.6 0.8 0.8 0.8
#> ASV_137 0.6 0.6 0.0 0.0 0.4 0.0 0.2 0.2
#> ASV_130 0.0 0.0 0.0 0.2 0.2 0.0 0.0 0.2
#> ASV_107 0.0 0.0 0.8 0.6 0.6 0.0 0.0 0.8
#> ASV_115 0.0 0.0 0.0 0.0 1.0 0.2 0.2 0.0
#> ASV_118 0.2 0.0 0.8 0.0 0.0 0.8 0.4 0.6
#> ASV_70 0.6 0.4 0.8 0.8 0.8 0.6 0.4 1.0
#> ASV_103 0.4 0.2 0.2 0.0 0.0 0.6 0.8 0.8
#> ASV_133 0.8 0.0 0.8 0.6 0.8 0.8 0.2 0.6
#> ASV_23 0.4 0.2 0.0 0.0 0.0 0.6 0.8 0.2
#> ASV_42 0.2 0.8 0.0 0.0 0.0 0.6 0.8 0.4
#> ASV_45 0.2 0.4 0.0 0.0 0.0 1.0 0.6 0.6
#> ASV_140 0.0 0.0 0.4 0.0 0.0 0.2 0.8 0.0
#> ASV_97 0.0 0.0 0.0 0.2 0.0 0.4 0.0 0.6
#> ASV_29 0.0 0.0 0.0 0.0 0.0 0.8 0.0 0.0
#> ASV_147 1.0 0.8 0.4 0.4 0.0 0.2 0.2 0.4
#> ASV_54 0.6 0.2 0.8 0.6 1.0 0.2 0.0 0.8
#> ASV_15 0.0 0.4 0.8 0.8 0.4 0.0 0.6 1.0
#> ASV_156 0.0 0.8 1.0 0.6 0.4 0.0 0.2 0.2
#> ASV_132 0.0 0.8 0.2 0.4 0.6 0.0 0.4 0.4
#> ASV_22 0.0 0.0 0.8 1.0 0.8 0.4 0.4 0.4
#> ASV_68 0.0 0.0 0.0 0.4 0.2 0.6 0.2 0.8
#> ASV_59 0.2 0.4 0.0 0.4 0.2 0.8 0.4 0.0
#> ASV_87 0.8 0.0 0.0 0.0 0.2 0.2 0.4 0.2
#> ASV_74 0.8 0.6 0.4 0.6 0.0 0.2 0.4 0.2
#> ASV_65 0.6 0.6 0.2 1.0 0.2 1.0 0.2 0.0
#> ASV_66 0.0 0.2 0.0 0.0 0.0 0.8 0.0 0.0
#> ASV_101 0.0 0.2 0.2 0.0 0.0 0.2 0.0 0.0
#> ASV_122 0.0 0.0 0.2 0.0 0.0 0.6 0.4 0.0
#> ASV_163 0.0 0.0 0.4 0.0 0.0 0.6 0.0 0.0
#> ASV_32 0.0 0.0 0.0 0.0 0.2 0.8 0.2 0.8
#> ASV_128 0.0 0.0 0.0 0.0 0.2 0.6 0.2 0.4
#> ASV_33 0.0 0.2 0.0 0.0 0.0 0.2 0.4 0.0
#> ASV_77 0.0 0.2 0.0 0.0 0.0 0.8 0.2 0.8
#> ASV_159 0.0 0.8 0.0 0.0 0.0 0.0 0.4 0.2
#> ASV_46 0.6 0.6 0.0 0.4 0.8 1.0 0.6 1.0
#> ASV_112 0.6 0.4 0.4 0.0 0.2 0.2 0.6 0.8
#> ASV_89 0.2 0.8 0.0 0.0 0.6 0.8 0.4 0.6
#> ASV_111 0.0 0.4 0.0 0.0 0.2 0.8 0.8 0.6
#> ASV_37 0.4 0.2 0.0 0.2 0.2 0.2 0.8 1.0
#> ASV_1 0.8 0.0 0.6 0.2 0.6 0.6 0.4 0.8
#> ASV_86 0.6 0.6 0.6 0.6 0.2 0.6 0.2 1.0
#> ASV_18 0.2 0.0 1.0 0.6 0.2 0.6 0.8 0.8
#> ASV_34 1.0 1.0 0.4 0.4 0.6 0.6 0.2 0.8
#> ASV_25 0.2 0.6 0.0 0.2 0.0 0.0 0.4 0.0
#> ASV_124 0.0 0.4 0.0 0.4 0.6 0.6 0.6 0.8
#> ASV_119 0.2 0.6 0.2 0.0 0.4 0.8 0.6 1.0
#> ASV_153 0.0 0.6 0.0 0.8 0.4 0.2 1.0 0.6
#> ASV_52 0.4 0.8 0.2 0.4 0.4 0.0 0.8 0.6
#> ASV_21 1.0 0.4 0.8 0.6 0.4 0.8 0.4 0.6
#> ASV_49 0.0 1.0 0.6 0.2 0.0 0.2 0.6 0.2
#> ASV_53 0.2 0.8 0.0 0.4 0.0 0.0 0.2 0.2
#> ASV_138 0.2 0.8 0.8 0.8 0.2 0.2 0.4 0.0
#> ASV_16 0.2 0.6 0.6 0.6 0.4 0.4 0.2 0.0
#> ASV_95 0.4 0.6 1.0 0.8 0.2 0.4 0.8 0.8
#> ASV_9 0.6 1.0 0.6 0.6 0.4 0.6 0.4 0.6
#> ASV_135 0.2 0.2 0.4 0.2 0.0 0.4 0.0 0.0
#> ASV_31 0.8 0.4 0.4 0.4 0.2 0.8 0.2 0.8
#> ASV_91 0.2 0.0 0.6 0.2 1.0 0.2 1.0 0.0
#> ASV_67 0.6 0.4 0.2 0.0 0.4 0.8 0.4 0.2
#> ASV_43 0.0 0.2 0.0 0.0 0.0 0.8 0.8 0.0
#> ASV_172 0.0 0.2 0.0 0.0 0.0 0.8 0.0 0.2
#> ASV_51 0.4 0.2 0.2 0.4 0.4 0.2 0.6 0.0
#> ASV_117 0.4 1.0 1.0 1.0 0.8 0.0 0.0 0.0
#> ASV_8 0.6 1.0 0.2 0.4 0.4 0.0 0.4 0.6
#> ASV_106 0.8 0.2 0.4 0.0 0.0 0.4 0.8 0.0
#> ASV_125 0.2 0.2 0.0 0.2 1.0 0.4 0.2 0.6
#> ASV_113 0.6 0.0 0.0 0.6 0.2 0.6 0.2 0.8
#> ASV_109 0.0 0.0 0.0 0.6 0.0 0.4 0.2 0.0
#> ASV_26 0.0 0.0 0.0 0.2 0.0 0.6 0.4 0.6
#> ASV_154 0.2 0.6 0.0 0.2 0.4 0.2 0.4 0.0
#> ASV_88 0.0 0.0 0.0 0.0 0.4 0.2 0.0 0.0
#> ASV_151 0.0 0.2 0.2 0.6 1.0 1.0 0.4 0.2
#> ASV_27 0.0 0.0 0.0 0.0 0.0 0.6 0.0 0.6
#> ASV_148 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
#> ASV_162 0.0 1.0 0.0 0.0 0.0 1.0 0.4 0.4
#> ASV_142 0.0 0.0 1.0 0.0 0.0 0.0 0.2 0.2
#> ASV_121 0.0 0.0 0.0 1.0 0.0 0.4 0.2 0.4
#> ASV_126 0.0 0.0 0.0 0.0 1.0 0.2 0.6 1.0
#> ASV_78 0.0 1.0 0.0 0.4 0.2 1.0 0.0 0.8
#> ASV_19 0.0 0.4 0.2 0.2 0.6 0.0 1.0 0.6
#> ASV_84 0.0 0.4 0.2 0.4 1.0 0.8 0.6 1.0Code 6B（Example 33）
# BiocManager::install("WGCNA")
x = ps %>%
filter_OTU_ps(Top = 200) %>%
# scale_micro(method = "TMM") %>%
vegan_otu() %>%
t() %>%
as.data.frame()
occor<-WGCNA::corAndPvalue(t(x)/colSums(x))
mtadj<-multtest::mt.rawp2adjp(unlist(occor$p),proc='BH')
adpcor<-mtadj$adjp[order(mtadj$index),2]
occor.p<-matrix(adpcor,dim(t(x)/colSums(x))[2])
## R value
occor.r<-occor$cor
diag(occor.r) <- 0Code 6C（Example 34）
library(Hmisc)
df_corr <- rcorr(t(otu_table), type = 'spearman')
df_corr_r = df_corr$r
df_corr_p = df_corr$P
df_p <- p.adjust(df_corr_p, method = 'BH')Code 6D（Example 35）
library(SpiecEasi)
spmatrix <- SpiecEasi::sparcc(t(otu_table))
tp0 <- proc.time()
sp.boot <- SpiecEasi::sparccboot(
t(otu_table),
R = 10,
ncpus = 1
)
tp1 <- proc.time()
tp1 - tp0
#> user system elapsed
#> 31.54 0.50 64.60
sp.p <- SpiecEasi::pval.sparccboot(sp.boot, sided = "both")
cors <- sp.p$cors
sp.p$pvals[is.na(sp.p$pvals)] = 1
pvals <- sp.p$pvals
sparCCpcors <- diag(0.5, nrow = dim(spmatrix$Cor)[1], ncol = dim(spmatrix$Cor)[1])
sparCCpcors[upper.tri(sparCCpcors, diag=FALSE)] <- cors
sparCCpcors <- sparCCpcors + t(sparCCpcors)
sparCCpval <- diag(0.5, nrow = dim(spmatrix$Cor)[1], ncol = dim(spmatrix$Cor)[1])
sparCCpval[upper.tri(sparCCpval, diag=FALSE)] <- pvals
sparCCpval <- sparCCpval + t(sparCCpval)
dim(sparCCpval)
#> [1] 150 150
rownames(sparCCpcors) <- colnames(t(otu_table))
colnames(sparCCpcors) <- colnames(t(otu_table))
rownames(sparCCpval) <- colnames(t(otu_table))
colnames(sparCCpval) <- colnames(t(otu_table))Code 6E（Example 36）
library(Hmisc)
library(igraph)
library(ggClusterNet)
df_corr <- rcorr(t(otu_table), type = 'spearman')
df_corr_r = df_corr$r
igraph = make_igraph(df_corr_r)
num.edges <- length(E(igraph)) # length(curve_multiple(igraph))
num.edges
#> [1] 11162
# Order (number of vertices) of a graph
num.vertices <- length(V(igraph))# length(diversity(igraph, weights = NULL, vids = V(igraph)))
num.vertices
#> [1] 150
#
connectance <- edge_density(igraph,loops=FALSE)#
connectance
#> [1] 0.9988367
# (Average degree)
average.degree <- mean(igraph::degree(igraph))
average.degree
#> [1] 148.8267
# (Average path length)
if (!is.null(E(igraph)$weight)) {
igraph.weight <- E(igraph)$weight
E(igraph)$weight = abs(E(igraph)$weight)
}
average.path.length <- average.path.length(igraph) # mean_distance(igraph) # mean_distance calculates the average path length in a graph
average.path.length
#> [1] 0.01555895
# (Diameter)
diameter <- diameter(igraph, directed = FALSE, unconnected = TRUE, weights = NULL)
diameter
#> [1] 0.03878623Code 6F（Example 37）
# BiocManager::install("ggraph")
library(Hmisc)
library(ggraph)
df_corr <- rcorr(t(otu_table), type = 'spearman')
df_corr_r = df_corr$r
df_corr_p = df_corr$P
df_p <- p.adjust(df_corr_p, method = 'BH')
df_corr_r[df_corr_p>0.05|abs(df_corr_r)<0.6] = 0
igraph = make_igraph(df_corr_r)
p = ggraph(igraph) +
geom_edge_link(color = "blue") +
geom_node_point(color = "red") +
theme_void()
p
set.seed(12)
plot(igraph,main="Co-occurrence network",
vertex.frame.color=NA,
edge.lty=1,
edge.curved=TRUE,
vertex.size=3,
pch = 21,
margin=c(0,0,0,0),
vertex.label.cex=.1,
vertex.label.dist=0.1,#标签大小
layout=layout_in_circle#控制样式，具体见官方文档
)
Code 6G（Example 38）
# BiocManager::install("tidyfst")
result = corMicro (ps = ps,
N = 150,
method.scale = "TMM",
r.threshold=0.8,
p.threshold=0.05,
method = "spearman"
)
cor = result[[1]]
ps_net = result[[3]]
otu_table = ps_net %>%
vegan_otu() %>%
t() %>%
as.data.frame()
tax_table = ps_net %>%
vegan_tax() %>%
as.data.frame()
netClu = data.frame(ID = row.names(tax_table),group =rep(1,length(row.names(tax_table)))[1:length(row.names(tax_table))] )
netClu$group = as.factor(netClu$group)
library(sna)
result2 = PolygonClusterG (cor = cor,nodeGroup =netClu )
node = result2[[1]]
nodes = nodeadd(plotcord =node,otu_table = otu_table,tax_table = tax_table)
edge = edgeBuild(cor = cor,node = node)
pnet <- ggplot() + geom_segment(aes(x = X1, y = Y1, xend = X2, yend = Y2,color = as.factor(cor)),
data = edge, size = 0.5) +
geom_point(aes(X1, X2,fill = Phylum,size = mean),pch = 21, data = nodes) +
scale_colour_brewer(palette = "Set1") +
scale_x_continuous(breaks = NULL) + scale_y_continuous(breaks = NULL) +
# labs( title = paste(layout,"network",sep = "_"))+
# geom_text_repel(aes(X1, X2,label=Phylum),size=4, data = plotcord)+
# discard default grid + titles in ggplot2
theme(panel.background = element_blank()) +
# theme(legend.position = "none") +
theme(axis.title.x = element_blank(), axis.title.y = element_blank()) +
theme(legend.background = element_rect(colour = NA)) +
theme(panel.background = element_rect(fill = "white", colour = NA)) +
theme(panel.grid.minor = element_blank(), panel.grid.major = element_blank())
pnet
dat = net_properties.2(igraph,n.hub = T)
head(dat,n = 16)
#> value
#> num.edges(L) 770.0000000
#> num.pos.edges 570.0000000
#> num.neg.edges 200.0000000
#> num.vertices(n) 137.0000000
#> Connectance(edge_density) 0.0826535
#> average.degree(Average K) 11.2408759
#> average.path.length 2.1468442
#> diameter 5.7236830
#> edge.connectivity 1.0000000
#> mean.clustering.coefficient(Average.CC) 0.4753316
#> no.clusters 1.0000000
#> centralization.degree 0.2041112
#> centralization.betweenness 0.1276547
#> centralization.closeness 0.2339196
#> RM(relative.modularity) 0.2461952
#> the.number.of.keystone.nodes 21.0000000
nodepro = node_properties(igraph)
head(nodepro)
#> igraph.degree igraph.closeness igraph.betweenness igraph.cen.degree
#> ASV_28 5 0.2523191 72.133269 5
#> ASV_86 4 0.2398589 6.350794 4
#> ASV_21 7 0.2666667 364.712265 7
#> ASV_108 1 0.1940086 0.000000 1
#> ASV_34 6 0.2893617 467.994217 6
#> ASV_18 3 0.2509225 8.165545 3
result = random_Net_compate(igraph = igraph, type = "gnm", step = 100, netName = layout)
p1 = result[[1]]
sum_net = result[[4]]
p1
Functional predict
Code 7A （Example 39）
Tax4Fun工具目前被Tax4Fun2所替带，这里示例内容省略，推荐使用编号4的代码运行Tax4Fun2内容。
# library(Tax4Fun)
# ###input data
# QIIMESingleData <- importQIIMEData("otu_taxa_table.txt")
# #QIIMESingleData <-importQIIMEBiomData("HMP_0.97_table.txt")
#
# otu_table = QIIMESingleData$otuTable
# colSums(otu_table)
#
# write.table("ID\t", file="otu_table_tax.txt",append = FALSE, quote = FALSE, sep="\t",eol = "", na = "NA", dec = ".", row.names = F,col.names = F)
# write.table(otu_table, file="otu_table_tax.txt",append = T, quote = FALSE, sep="\t",eol = "\n", na = "NA", dec = ".", row.names = TRUE,col.names = TRUE)
#
# ##KO_all
# Tax4FunOutput <- Tax4Fun(QIIMESingleData, "/EMCDisk1/Micro/Test/R/Tax4Fun/SILVA/SILVA123", fctProfiling = TRUE, refProfile = "UProC", shortReadMode = TRUE, normCopyNo = TRUE)
# KO_table = t(Tax4FunOutput$Tax4FunProfile)
# colSums(KO_table)
# write.table("ID\t", file="KO_table.txt",append = FALSE, quote = FALSE, sep="\t",eol = "", na = "NA", dec = ".", row.names = F,col.names = F)
# write.table(KO_table, file="KO_table.txt",append = T, quote = FALSE, sep="\t",eol = "\n", na = "NA", dec = ".", row.names = TRUE,col.names = TRUE)Code 7B Example 41
Example 41
Tax4Fun2功能预测
# # devtools::install_github("fjossandon/Tax4Fun2")
# source("./4.The best practice R pipeline/function/runRefBlast2.R")
# funcpath = paste("./Tax4Fun2/",sep = "")
# dir.create(funcpath)
#
# path_to_reference_data = "./4.The best practice R pipeline/Tax4Fun2/Tax4Fun2_ReferenceData_v2"
# otudir = funcpath
# #加载
# library(Tax4Fun2)
# #物种注释
# # #指定 OTU 代表序列、Tax4Fun2 库的位置、参考数据库版本、序列比对（blastn）线程数等
# runRefBlast2(path_to_otus = './data/otus2.fa',
# path_to_reference_data = path_to_reference_data,
# path_to_temp_folder = otudir, database_mode = 'Ref100NR',
# use_force = TRUE, num_threads = 4)
# #预测群落功能
# #指定 OTU 丰度表、Tax4Fun2 库的位置、参考数据库版本、上步的物种注释结果路径等
# makeFunctionalPrediction(path_to_otu_table = './data/otutab.txt',
# path_to_reference_data = path_to_reference_data,
# path_to_temp_folder = otudir,
# database_mode = 'Ref100NR',
# normalize_by_copy_number = TRUE,
# min_identity_to_reference = 0.97,
# normalize_pathways = FALSE)Other microbial analysis
Code 8A Example 42
# BiocManager::install("eulerr")
# BiocManager::install("minpack.lm")
library(picante)
library(ape)
library(vegan)
library(FSA)
library(eulerr)
library(grid)
library(gridExtra)
require(minpack.lm)
require(Hmisc)
require(stats4)
library(parallel)
library(tidyverse)
set.seed(72)
psrare = rarefy_even_depth(ps)
ps.norm = transform_sample_counts(psrare, function(x) x/sum(x))
map = as.data.frame(sample_data(psrare))
aa = levels(map$Group)
aa
#> [1] "KO" "OE" "WT"
map$ID = row.names(map)
plots = list()
dat1 = list()
dat2 = list()
i =1
for (i in 1:length(aa)) {
maps<- dplyr::filter(as.tibble(map),Group %in%aa[i])
maps = as.data.frame(maps)
row.names(maps) = maps$ID
ps_sub = psrare
sample_data( ps_sub ) =maps ;ps_sub
OTU.table = t(otu_table(ps_sub))
head(OTU.table )
N <- mean(apply(OTU.table, 1, sum))
p.m <- apply(OTU.table, 2, mean)
p.m <- p.m[p.m != 0]
p <- p.m/N
p.df = data.frame(p) %>%
rownames_to_column(var="OTU")
OTU.table.bi <- 1*(OTU.table>0)
freq.table <- apply(OTU.table.bi, 2, mean)
freq.table <- freq.table[freq.table != 0]
freq.df = data.frame(OTU=names(freq.table), freq=freq.table)
#Combine
C <- inner_join(p.df,freq.df, by="OTU") %>%
arrange(p)
# Remove rows with any zero (absent in either source pool or local communities). You already did this, but just to make sure we will do it again.
C.no0 <- C %>%
filter(freq != 0, p != 0)
#Calculate the limit of detection
d <- 1/N
##Fit model parameter m (or Nm) using Non-linear least squares (NLS)
p.list <- C.no0$p
freq.list <- C.no0$freq
m.fit <- nlsLM(freq.list ~ pbeta(d, N*m*p.list, N*m*(1-p.list), lower.tail=FALSE), start=list(m=0.1))
m.ci <- confint(m.fit, 'm', level=0.95)
m.sum <- summary(m.fit)
m.coef = coef(m.fit)
freq.pred <- pbeta(d, N*coef(m.fit)*p.list, N*coef(m.fit)*(1-p.list), lower.tail=FALSE)
Rsqr <- 1 - (sum((freq.list - freq.pred)^2))/(sum((freq.list - mean(freq.list))^2))
# Get table of model fit stats
fitstats <- data.frame(m=m.coef, m.low.ci=m.ci[1], m.up.ci=m.ci[2],
Rsqr=Rsqr, p.value=m.sum$parameters[4], N=N,
Samples=nrow(OTU.table), Richness=length(p.list),
Detect=d)
# Get confidence interval for predictions
freq.pred.ci <- binconf(freq.pred*nrow(OTU.table), nrow(OTU.table), alpha=0.05, method="wilson", return.df=TRUE)
# Get table of predictions
pred.df <- data.frame(metacomm_RA=p.list, frequency=freq.pred,
frequency_lowerCI=freq.pred.ci[,2],
frequency_upperCI=freq.pred.ci[,3]) %>%
unique()
# Get table of observed occupancy and abundance
obs.df = C.no0 %>%
dplyr::rename(metacomm_RA = p, frequency=freq)
head(obs.df)
p = ggplot(data=obs.df) +
geom_point(data=obs.df, aes(x=log10(metacomm_RA), y=frequency),
alpha=.3, size=2, color="#8DD3C7") +
geom_line(data=pred.df, aes(x=log10(metacomm_RA), y=frequency), color="#FFFFB3") +
geom_line(data=pred.df, aes(x=log10(metacomm_RA), y=frequency_lowerCI), linetype=2, color="#FFFFB3") +
geom_line(data=pred.df, aes(x=log10(metacomm_RA), y=frequency_upperCI), linetype=2, color="#FFFFB3") +
# geom_text(data=fitstats, aes(label = paste("R^2 == ", round(Rsqr, 3))),
# x=1, y=0.75, size=4, parse=TRUE) +
# geom_text(data=fitstats, aes(label = paste("italic(m) ==", round(m, 3))),
# x=-1, y=0.85, size=4, parse=TRUE) +
labs(x="Log10 abundance in\nmetacommunity", y="Frequency detected",title = paste(aa[i],paste("R^2 == ", round(fitstats$Rsqr, 3)),paste("italic(m) ==", round(fitstats$m, 3)))) +
theme_bw() +
theme(axis.line = element_line(color="black"),
legend.position = "none",
axis.title = element_text(size=14),
axis.text = element_text(size=12))
p
plots[[aa[i]]] = p
dat1[[aa[i]]] = obs.df
dat2[[aa[i]]] = pred.df
}
p = ggpubr::ggarrange(plotlist = plots,common.legend = TRUE, legend="right",ncol =3,nrow = 1)
p
BNTI
num = 10
ps_sub <- ps %>% filter_OTU_ps(200)
map = as.data.frame(sample_data(ps_sub))
map$ID = row.names(map)
sample_data(ps) = map
set.seed(72) # setting seed for reproducibility
psrare = rarefy_even_depth(ps_sub)
sample_sums(psrare)
#> KO1 KO2 KO3 KO4 KO5 KO6 OE1 OE2 OE3 OE4 OE5 OE6 WT1
#> 17963 17963 17963 17963 17963 17963 17963 17963 17963 17963 17963 17963 17963
#> WT2 WT3 WT4 WT5 WT6
#> 17963 17963 17963 17963 17963
ps.norm = transform_sample_counts(psrare, function(x) x/sum(x))
bMNTD_null_func <- function(i, OTU.table, tree){
tree$tip.label = sample(tree$tip.label)
bMNTD_s = comdistnt(OTU.table, cophenetic(tree), abundance.weighted = TRUE)
A <- attr(bMNTD_s, "Size")
B <- if (is.null(attr(bMNTD_s, "Labels"))) sequence(A) else attr(bMNTD_s, "Labels")
if (isTRUE(attr(bMNTD_s, "Diag"))) attr(bMNTD_s, "Diag") <- FALSE
if (isTRUE(attr(bMNTD_s, "Upper"))) attr(bMNTD_s, "Upper") <- FALSE
bMNTD_s.df = data.frame(Sample_1 = B[unlist(lapply(sequence(A)[-1], function(x) x:A))],
Sample_2 = rep(B[-length(B)], (length(B)-1):1),
bMNTD = as.vector(bMNTD_s),
rep=i)
return(bMNTD_s.df)
}
# 计算βNTI
Phylo_turnover <- function(physeq, reps, nproc){
# Extract OTU table
OTU.table = t(otu_table(physeq))
# Extract phylogenetic tree
tree = phy_tree(physeq)
# Get βMNTD between all communities
bMNTD_o = comdistnt(OTU.table, cophenetic(tree), abundance.weighted = TRUE)
A <- attr(bMNTD_o, "Size")
B <- if (is.null(attr(bMNTD_o, "Labels"))) sequence(A) else attr(bMNTD_o, "Labels")
if (isTRUE(attr(bMNTD_o, "Diag"))) attr(bMNTD_o, "Diag") <- FALSE
if (isTRUE(attr(bMNTD_o, "Upper"))) attr(bMNTD_o, "Upper") <- FALSE
bMNTD_o.df = data.frame(Sample_1 = B[unlist(lapply(sequence(A)[-1], function(x) x:A))],
Sample_2 = rep(B[-length(B)], (length(B)-1):1),
bMNTD = as.vector(bMNTD_o))
# Get βMNTD for randomized null communities
rep.list = seq(1, reps)
bMNTD_s.df.list = mclapply(rep.list, bMNTD_null_func, OTU.table=OTU.table, tree=tree, mc.cores=nproc)
# Combine all data together and calculate βNTI for each sample pair
bMNTD_s.df <- do.call("rbind", bMNTD_s.df.list)
bMNTD_s.means.df = bMNTD_s.df %>%
group_by(Sample_1, Sample_2) %>%
dplyr::summarize(mean_bMNTD = mean(bMNTD),
sd_bMNTD = sd(bMNTD))
bMNTD_o.df = inner_join(bMNTD_o.df, bMNTD_s.means.df, by=c("Sample_1", "Sample_2")) %>%
mutate(bNTI = (bMNTD - mean_bMNTD)/sd_bMNTD)
return(bMNTD_o.df)
}
bNTI = Phylo_turnover(psrare, 10, 1)phypath = "./2.Microbial community analysis/"
RCbary = function(otu = NULL,tax = NULL,map = NULL,tree = NULL ,ps = NULL,group = "Group",num = 99,thread = 1){
ps_sub <- ps
#----------------整理map文件
map = as.data.frame(sample_data(ps_sub))
map$ID = row.names(map)
sample_data(ps) = map
#-------------------准备OTU表格
#-----------------抽平-不设置抽平条数，默认按照最小序列数数目抽平
set.seed(72) # setting seed for reproducibility
psrare = rarefy_even_depth(ps_sub )
#检查序列数量
sample_sums(psrare)
# 标准化数据
ps.norm = transform_sample_counts(psrare, function(x) x/sum(x))
#--------------两个函数
# 对模拟群落计算距离
RCbray_null_func <- function(i, freq.abd.df, alpha1, alpha2, N){
# Get simulated communities and distance
## initally select OTUs weighted by their frequency. The number of OTUs selected should equal the richness of the samples.
simcom1 = data.frame(table(sample(freq.abd.df$OTU, size=alpha1, replace=F, prob=freq.abd.df$freq)), stringsAsFactors = F)
colnames(simcom1) = c("OTU","simcom1")
simcom1$OTU = as.character(simcom1$OTU)
simcom1 = inner_join(simcom1, freq.abd.df, by="OTU")
simcom2 = data.frame(table(sample(freq.abd.df$OTU, size=alpha2, replace=F, prob=freq.abd.df$freq)), stringsAsFactors = F)
colnames(simcom2) = c("OTU","simcom2")
simcom2$OTU = as.character(simcom2$OTU)
simcom2 = inner_join(simcom2, freq.abd.df, by="OTU")
## Now recruit OTUs based on their abundance in the metacommunity
simcom1.abd = data.frame(table(sample(simcom1$OTU, size=N-alpha1, replace=T, prob=simcom1$p)), stringsAsFactors = F)
colnames(simcom1.abd) = c("OTU","simcom1.abd")
simcom1.abd$OTU = as.character(simcom1.abd$OTU)
simcom1 = full_join(simcom1, simcom1.abd, by="OTU") %>%
mutate(simcom1.abd = ifelse(is.na(simcom1.abd), 1, simcom1.abd)) %>%
select(OTU, simcom1.abd)
simcom2.abd = data.frame(table(sample(simcom2$OTU, size=N-alpha2, replace=T, prob=simcom2$p)), stringsAsFactors = F)
colnames(simcom2.abd) = c("OTU","simcom2.abd")
simcom2.abd$OTU = as.character(simcom2.abd$OTU)
simcom2 = full_join(simcom2, simcom2.abd, by="OTU") %>%
mutate(simcom2.abd = ifelse(is.na(simcom2.abd), 1, simcom2.abd)) %>%
select(OTU, simcom2.abd)
simcom = full_join(simcom1, simcom2, by="OTU")
simcom[is.na(simcom)] = 0
rownames(simcom) = simcom$OTU
simcom$OTU = NULL
null.dist = vegdist(t(simcom), method="bray")[1]
return(null.dist)
}
# 计算RCbray的主功能
Calc_RCbray <- function(physeq, reps, nproc){
# Get OTU table from phyloseq object
otu.table = otu_table(physeq)
# Get alpha diversity for each sample
otu.PA.table = otu.table
otu.PA.table[otu.PA.table > 0] = 1
alpha.df = data.frame(Sample_ID = colnames(otu.PA.table), OTU.n = colSums(otu.PA.table), stringsAsFactors = F)
# Get beta diversity matrix
beta.table = as.matrix(vegdist(t(otu.PA.table), method="bray", diag=TRUE, upper=TRUE))
## Get metacommunity
# Calculate the number of individuals in the meta community (Average read depth)
N <- mean(apply(t(otu.table), 1, sum))
# Calculate the average relative abundance of each taxa across communities
p.m <- apply(t(otu.table), 2, mean)
p.m <- p.m[p.m != 0]
p <- p.m/N
# Calculate the occurrence frequency of each taxa across communities
otu.table.bi <- 1*(t(otu.table)>0)
freq <- apply(otu.table.bi, 2, mean)
freq <- freq[freq != 0]
# Combine
freq.abd.df = data.frame(p=p, freq=freq) %>%
tibble::rownames_to_column(var="OTU") %>%
filter(p != 0, freq != 0) %>%
arrange(p)
# For each pair of samples run the RCbray analysis
comps = combn(alpha.df$Sample_ID, m=2, simplify = F)
RCb.df = data.frame(Site1 = character(), Site2 = character(), RCb = numeric(), stringsAsFactors = F)
for (j in seq(1, length(comps))){
sam = comps[[j]]
alpha1 = alpha.df[alpha.df$Sample_ID == sam[1],]$OTU.n
alpha2 = alpha.df[alpha.df$Sample_ID == sam[2],]$OTU.n
# Permute "reps" many times
rep.list = seq(1, reps)
null.list = mclapply(rep.list, RCbray_null_func, freq.abd.df=freq.abd.df, alpha1=alpha1, alpha2=alpha2, N=N, mc.cores=nproc)
RCb = (length(null.list[null.list > beta.table[sam[1], sam[2]]]) + (0.5*length(null.list[null.list == beta.table[sam[1], sam[2]]])))/reps
RCb = (RCb - 0.5)*2
RCb.df = rbind(RCb.df, data.frame(Site1=sam[1], Site2=sam[2], RCb=RCb, stringsAsFactors = F))
}
RCb.df
return(RCb.df)
}
# 运行RCbray的计算，这个运算再5个小时左右999重复
RCb = Calc_RCbray(psrare, num, thread)
head(RCb)
return(list(RCb))
}
result = RCbary(ps = ps%>% filter_OTU_ps(200) ,group = "Group",num = 10,thread = 1)
RCbary = result[[1]]
head(RCbary)filename = paste(phypath,"/5_RCb.csv",sep = "")
write.csv(RCbary,filename)
#---二
ps_sub <- ps%>% filter_OTU_ps(200)
#----------------整理map文件
map = as.data.frame(sample_data(ps_sub))
map$ID = row.names(map)
sample_data(ps) = map
set.seed(72) # setting seed for reproducibility
psrare = rarefy_even_depth(ps_sub )
#检查序列数量
sample_sums(psrare)
#> KO1 KO2 KO3 KO4 KO5 KO6 OE1 OE2 OE3 OE4 OE5 OE6 WT1
#> 17963 17963 17963 17963 17963 17963 17963 17963 17963 17963 17963 17963 17963
#> WT2 WT3 WT4 WT5 WT6
#> 17963 17963 17963 17963 17963
# 标准化数据
ps.norm = transform_sample_counts(psrare, function(x) x/sum(x))
#--------------两个函数
# 对模拟群落计算距离
RCbray_null_func <- function(i, freq.abd.df, alpha1, alpha2, N){
# Get simulated communities and distance
## initally select OTUs weighted by their frequency. The number of OTUs selected should equal the richness of the samples.
simcom1 = data.frame(table(sample(freq.abd.df$OTU, size=alpha1, replace=F, prob=freq.abd.df$freq)), stringsAsFactors = F)
colnames(simcom1) = c("OTU","simcom1")
simcom1$OTU = as.character(simcom1$OTU)
simcom1 = inner_join(simcom1, freq.abd.df, by="OTU")
simcom2 = data.frame(table(sample(freq.abd.df$OTU, size=alpha2, replace=F, prob=freq.abd.df$freq)), stringsAsFactors = F)
colnames(simcom2) = c("OTU","simcom2")
simcom2$OTU = as.character(simcom2$OTU)
simcom2 = inner_join(simcom2, freq.abd.df, by="OTU")
## Now recruit OTUs based on their abundance in the metacommunity
simcom1.abd = data.frame(table(sample(simcom1$OTU, size=N-alpha1, replace=T, prob=simcom1$p)), stringsAsFactors = F)
colnames(simcom1.abd) = c("OTU","simcom1.abd")
simcom1.abd$OTU = as.character(simcom1.abd$OTU)
simcom1 = full_join(simcom1, simcom1.abd, by="OTU") %>%
mutate(simcom1.abd = ifelse(is.na(simcom1.abd), 1, simcom1.abd)) %>%
select(OTU, simcom1.abd)
simcom2.abd = data.frame(table(sample(simcom2$OTU, size=N-alpha2, replace=T, prob=simcom2$p)), stringsAsFactors = F)
colnames(simcom2.abd) = c("OTU","simcom2.abd")
simcom2.abd$OTU = as.character(simcom2.abd$OTU)
simcom2 = full_join(simcom2, simcom2.abd, by="OTU") %>%
mutate(simcom2.abd = ifelse(is.na(simcom2.abd), 1, simcom2.abd)) %>%
select(OTU, simcom2.abd)
simcom = full_join(simcom1, simcom2, by="OTU")
simcom[is.na(simcom)] = 0
rownames(simcom) = simcom$OTU
simcom$OTU = NULL
null.dist = vegdist(t(simcom), method="bray")[1]
return(null.dist)
}
# 计算RCbray的主功能
Calc_RCbray <- function(physeq, reps, nproc){
# Get OTU table from phyloseq object
otu.table = otu_table(physeq)
# Get alpha diversity for each sample
otu.PA.table = otu.table
otu.PA.table[otu.PA.table > 0] = 1
alpha.df = data.frame(Sample_ID = colnames(otu.PA.table), OTU.n = colSums(otu.PA.table), stringsAsFactors = F)
# Get beta diversity matrix
beta.table = as.matrix(vegdist(t(otu.PA.table), method="bray", diag=TRUE, upper=TRUE))
## Get metacommunity
# Calculate the number of individuals in the meta community (Average read depth)
N <- mean(apply(t(otu.table), 1, sum))
# Calculate the average relative abundance of each taxa across communities
p.m <- apply(t(otu.table), 2, mean)
p.m <- p.m[p.m != 0]
p <- p.m/N
# Calculate the occurrence frequency of each taxa across communities
otu.table.bi <- 1*(t(otu.table)>0)
freq <- apply(otu.table.bi, 2, mean)
freq <- freq[freq != 0]
# Combine
freq.abd.df = data.frame(p=p, freq=freq) %>%
tibble::rownames_to_column(var="OTU") %>%
filter(p != 0, freq != 0) %>%
arrange(p)
# For each pair of samples run the RCbray analysis
comps = combn(alpha.df$Sample_ID, m=2, simplify = F)
RCb.df = data.frame(Site1 = character(), Site2 = character(), RCb = numeric(), stringsAsFactors = F)
for (j in seq(1, length(comps))){
sam = comps[[j]]
alpha1 = alpha.df[alpha.df$Sample_ID == sam[1],]$OTU.n
alpha2 = alpha.df[alpha.df$Sample_ID == sam[2],]$OTU.n
# Permute "reps" many times
rep.list = seq(1, reps)
null.list = mclapply(rep.list, RCbray_null_func, freq.abd.df=freq.abd.df, alpha1=alpha1, alpha2=alpha2, N=N, mc.cores=nproc)
RCb = (length(null.list[null.list > beta.table[sam[1], sam[2]]]) + (0.5*length(null.list[null.list == beta.table[sam[1], sam[2]]])))/reps
RCb = (RCb - 0.5)*2
RCb.df = rbind(RCb.df, data.frame(Site1=sam[1], Site2=sam[2], RCb=RCb, stringsAsFactors = F))
}
RCb.df
return(RCb.df)
}
# 运行RCbray的计算，这个运算再5个小时左右999重复
RCb = Calc_RCbray(psrare, 10, 1)
head(RCb)Code 8B Example 43
iCAMP
Code 8C Example 44
系统发育信号
source("./4.The best practice R pipeline/function/phyloSignal_and_phySigplot.R")
phypath = "./2.Microbial community analysis/"
phypath2 = paste(phypath,"/phyloSignal/",sep = "")
dir.create(phypath)
phyloSignal(ps = ps%>% filter_OTU_ps(200),
group = "Group",
env = env1[,1:2],
path = phypath2)
#> [1] "Now running KO"
#> [1] "Now running OE"
#> [1] "Now running WT"
result = phySigPlot(ps = ps%>% filter_OTU_ps(200),
group = "Group",env = env1[,1:2],
path = phypath2)
p2 = result[[1]]
p2
data = result[[2]]
head(data)Code 8D Example 45
library(phyloseq)
data(ps)
Envnetplot<- paste("./16s_Env_network",sep = "")
dir.create(Envnetplot)
ps.merge <- ggClusterNet::merge16S_ITS(ps16s = ps,
psITS = NULL,
N16s = 200)
ps.merge
#> phyloseq-class experiment-level object
#> otu_table() OTU Table: [ 200 taxa and 18 samples ]
#> sample_data() Sample Data: [ 18 samples by 11 sample variables ]
#> tax_table() Taxonomy Table: [ 200 taxa by 8 taxonomic ranks ]
map = phyloseq::sample_data(ps.merge)
# head(map)
map$Group = "one"
phyloseq::sample_data(ps.merge) <- map
envRDA.s = vegan::decostand(env1,"hellinger")
data1 = envRDA.s %>% rownames_to_column("id")
Gru = data.frame(ID = colnames(env1),group = "env" )
head(Gru)# library(sna)
# library(ggClusterNet)
# library(igraph)
result <- ggClusterNet::corBionetwork(ps = ps.merge,
N = 0,
r.threshold = 0.4,
p.threshold = 0.05,
big = T,
group = "Group",
env = data1,
envGroup = Gru,
# layout = "fruchtermanreingold",
path = Envnetplot,
fill = "Phylum",
size = "igraph.degree",
scale = TRUE,
bio = TRUE,
zipi = F,
step = 100,
width = 18,
label = TRUE,
height = 10
)
#> [1] "one"
#> num [1:10, 1:18] 0.0926 0.1167 0.043 0.0908 0.3555 ...
#> - attr(*, "dimnames")=List of 2
#> ..$ : chr [1:10] "env1" "env2" "env3" "env4" ...
#> ..$ : chr [1:18] "KO1" "KO2" "KO3" "KO4" ...
#> [1] "1"
#> [1] "2"
#> [1] "3"
p = result[[1]]
p
data = result[[2]]####Code 8E Example 46
FEAST功能预测
library(ggClusterNet)
data(ps)
source("./4.The best practice R pipeline/function/FEAST.R",encoding = "UTF-8")
# sample_data(ps)
result = FEAST(ps = ps,
group = "Group",
sinkG = "WT",
sourceG = c("OE","KO"),
path = "./4.The best practice R pipeline/function/" # 注意按照自己设定的路径进行修改
)
#> [1] "Number of OTUs in the sink sample = 2279"
#> [1] "Seq depth in the sources and sink samples = 32437"
#> [1] "The sink is: WT1"
#> [1] "Source mixing proportions"
#> KO1 OE1 unknown
#> 0.4799722 0.2391447 0.2808831
#> [1] "Number of OTUs in the sink sample = 2087"
#> [1] "Seq depth in the sources and sink samples = 33188"
#> [1] "The sink is: WT2"
#> [1] "Source mixing proportions"
#> KO2 OE2 unknown
#> 0.4297478 0.2606299 0.3096223
#> [1] "Number of OTUs in the sink sample = 2445"
#> [1] "Seq depth in the sources and sink samples = 34632"
#> [1] "The sink is: WT3"
#> [1] "Source mixing proportions"
#> KO3 OE3 unknown
#> 0.2590245 0.4782422 0.2627334
#> [1] "Number of OTUs in the sink sample = 2310"
#> [1] "Seq depth in the sources and sink samples = 32942"
#> [1] "The sink is: WT4"
#> [1] "Source mixing proportions"
#> KO4 OE4 unknown
#> 0.2340399 0.4633148 0.3026453
#> [1] "Number of OTUs in the sink sample = 2130"
#> [1] "Seq depth in the sources and sink samples = 33315"
#> [1] "The sink is: WT5"
#> [1] "Source mixing proportions"
#> KO5 OE5 unknown
#> 0.2867147 0.4517929 0.2614923
#> [1] "Number of OTUs in the sink sample = 2347"
#> [1] "Seq depth in the sources and sink samples = 32049"
#> [1] "The sink is: WT6"
#> [1] "Source mixing proportions"
#> KO6 OE6 unknown
#> 0.3545900 0.3794085 0.2660015
# result
p <- Plot_FEAST(data = result)
p
p2 = MuiPlot_FEAST(data = result)
p2